3 Species distribution and dispersal modelling

MONARCH 2 Report – Chapter 3 ___________________________________________________________________________

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3 Species distribution and dispersal modelling P.A. HARRISON, R.G. PEARSON, T.P. DAWSON, S. FREEMAN, J.E. HOSSELL,

H. LYONS, P. SCHOLEFIELD AND P.M. BERRY Summary Three models for analysing the impacts of climate and land cover change on potential species distribution are described. The SPECIES model employs an Artificial Neural Network (ANN) to characterise bioclimate envelopes based on inputs generated through a climate-hydrological process model. An important element of the model is its multi-scale approach whereby the bioclimate envelope of a species is first identified at the European scale before application at a finer resolution within Britain and Ireland. This enables climatic range margins that are currently outside Britain and Ireland, but which may move into these countries under future climate scenarios, to be identified. The model therefore does not extrapolate outside its training dataset when used to predict the suitable climate space for species in Britain and Ireland under potential future climates. The downscaled SPECIES model integrates the European scale climate-driven simulation with fine-scale land cover data in a second ANN to generate regional scale suitability surfaces for species. This provides an insight into the roles of climate and land cover as determinants of species’ distributions and enables predictions of distributions under scenarios of changing climate and land cover type to be examined either separately or together and for interactions to be explored. A generalised additive model is used to produce consistent scenarios of changes in key vegetative land cover types under climate change. The downscaling of the SPECIES model also facilitates coupling of the modelled species’ suitability surfaces with dynamic simulations of species dispersal. Species dispersal over a specified time step is simulated within the boundaries of the climate and land cover suitability surfaces. The model is based on a spatially explicit cellular automaton, which simulates the stochastic dispersal of species in terms of two main processes: the release of a number of propagules by an existing population and the redistribution of the propagules according to a dispersal function. The three inter-related species models produce maps of the probability of occurrence for each species; based on dispersal characteristics, climatic suitability and land cover for future scenarios. 3.1 Introduction Climate change and land cover change, leading to habitat loss, are two of the most important factors threatening ecosystems worldwide (Parmesan and Yohe, 2003, Sala et al., 2000). Considered individually, the threats posed are significant, but the interaction between the two factors could be disastrous (Travis 2003). A number of studies have modelled the potential impacts of climate change on the distribution of species, applying climate-driven simulations across a range of scales, study areas and species (for reviews see Guisan and Zimmermann, 2000; Pearson and Dawson, 2003). However, few modelling studies have explicitly addressed climate – land cover interactions. To study the combined effects of climate change and habitat fragmentation (as driven by changes in land cover), a novel modelling approach has been developed that integrates climate and land cover drivers in a scale-dependent hierarchical manner. The spatial scale at which species distribution modelling is undertaken is of fundamental importance, with the selection of appropriate spatial extent and data resolution for a given application essential. It has been proposed that climate impacts on the distribution of species will be most apparent at macro-scales, with broad spatial extents and coarse data resolutions most appropriate for correlating climate with species distributions. This is the premise behind the development of the SPECIES model, which uses an Artificial Neural Network (ANN) to simulate the potential climate space of species at the European scale (Pearson et al., 2002). It has also been hypothesised that within the climate space defined by synoptic climate conditions other factors influence the distribution of species in a hierarchical manner, with different factors being better correlates at different scales (Collingham et

MONARCH 2 Report – Chapter 3 ___________________________________________________________________________ 44

al., 2000; Franklin, 1995; Pearson and Dawson, 2003). Thus, it is proposed that land cover may be considered the dominant control of species presence and absence at a finer spatial resolution than climate. This is the premise behind the development of the downscaled SPECIES model, which integrates the broad-scale climate-driven simulation with finer-scale land cover data (Pearson et al., 2004). Outputs from the continental scale ANN are used as inputs to a second ANN, along with land cover data, to generate regional scale suitability surfaces for species. A suitability surface is defined as a landscape identifying areas where a species could potentially grow and reproduce, and is analogous to an approximation of the spatial manifestation of the fundamental niche. The downscaled SPECIES model has been designed to be applicable to scenarios of future climate and land cover change. Land cover change scenarios are based on the characterisation of climate envelopes for existing land cover classes and their subsequent projection under climate change. The downscaling of the SPECIES model also facilitates coupling of the modelled species’ suitability surfaces with dynamic simulations of species dispersal. Regional-scale predictions of potential future environmental impacts will be more suited to the requirements of local conservation policy planning than those of previous studies looking at the potential impacts and policy implications of broad-scale climate change, such as those investigated in MONARCH 1 (Harrison et al., 2001). 3.2 SPECIES model

The SPECIES (Spatial Estimator of the Climate Impacts on the Envelope of Species) model was used to simulate the impacts of climate change on the potential climatic suitability of individual species in the MONARCH 1 project (Berry et al., 2001). A full description of the model is given in Pearson et al. (2002). The model uses an ANN to integrate bioclimatic variables for predicting the distribution of species through the characterisation of bioclimatic envelopes. A number of integrated algorithms, including a climate-hydrological process model, are used to pre-process climate (temperature, precipitation, solar radiation, vapour pressure and wind speed) and soils (AWC – available water holding capacity) data to derive relevant bioclimatic variables for input to the neural network. Those variables found to be most successful for bird distributions (Harrison et al., 2003) and other taxa (Berry et al., 2003) are given in Table 3.1.

Table 3.1: Bioclimatic input variables used for birds and other taxa in the SPECIES model.

Birds Other taxa Growing degree days > 5°C Growing degree days > 5°C Absolute minimum temperature expected over a 20-year period

Absolute minimum temperature expected over a 20-year period

Mean summer temperature (May, June, July) Annual maximum temperature Mean summer precipitation (May, June, July) Accumulated annual soil water deficit Mean winter precipitation (December, January, February)

Accumulated annual soil water surplus

Mean summer water availability (May, June, July) The model is trained using existing empirical data on the European distributions of species to enable the full climate space of a species to be characterised and to capture their response to climatic conditions that might be expected under future scenarios. A kriging interpolation function is applied to the presence/absence distributions of each species to provide a smoothed suitability surface. The data are then randomly divided into three groups for training, validating and testing the neural network. The validation set ensures that the network does not over-train on the training data, thus losing its ability to generalise, while the test data is used to independently verify the prediction. Two methods for assessing the predictive performance of each network have been used: Cohen’s Kappa statistic of similarity (k) and the Area Under the Receiver Operating Characteristic Curve (AUC). Kappa is a commonly used statistic that provides a measure of proportional accuracy, adjusted for chance agreement (Cohen, 1960). Kappa varies from 0, indicating no agreement between


45

observed and predicted distributions, to 1 for perfect agreement. AUC is an unbiased measure of prediction accuracy calculated from the Receiver Operating Characteristic (ROC) curve. The ROC curve describes the compromise that is made between the sensitivity (defined as the proportion of true positive predictions versus the number of actual positive sites) and false positive fraction (the proportion of false positive predictions versus the number of actual negative sites). This index is independent of both species prevalence and the decision threshold (Fielding and Bell, 1997). AUC ranges from 0.5 for models with no discrimination ability, to 1 for models with perfect discrimination. Tables 3.2 and 3.3 show the statistical performance of the European neural network models at replicating the test dataset, which was not used for model training. The AUC statistic is greater than 0.9 for all European models, indicating very good discrimination ability. The maximum Kappa statistic is slightly lower for most species, but this is to be expected as the index can vary between 0 and 1. Here, 18 species show values greater than 0.85, indicating excellent agreement between observed and simulated distributions, 12 species show values between 0.7 and 0.85 indicating very good agreement, and 2 species show a value between 0.55 and 0.7 indicating good agreement.

Table 3.2: Statistics showing predictive performance of the SPECIES model for the bird species (see Table 2.10 for common names).

Species Kappa Numenius arquata 0.68 Pluvialis apricaria 0.86 Coccothraustes coccothraustes 0.74 Ficendula hypoleuca 0.81 Lagopus mutus 0.82 Parus montanus 0.81

Table 3.3: Statistics showing predictive performance of the SPECIES model for other taxa (see Table 2.10 for common names).

Species AUC Kappa Species AUC Kappa Hampshire, England: Snowdonia, Wales: Fraxinus excelsior 0.986 0.885 Quercus petraea 0.983 0.850 Fagus sylvatica 0.977 0.794 Melampyrum pratense 0.989 0.899 Mercurialis perennis 0.982 0.888 Hyacinthoides non-scripta 0.986 0.883 Apodemus flavicollis 0.960 0.714 Calluna vulgaris 0.991 0.895 Erica tetralix 0.986 0.865 Vaccinium myrtillus 0.988 0.903 Molinia caerulea 0.986 0.912 Carex bigelowii 0.953 0.697 Calluna vulgaris 0.991 0.895 Ulex gallii 0.996 0.760 Metrioptera brachyptera 0.992 0.907 Pteridium aquilinum 0.993 0.940 Central Highlands, Scotland: Cuilcagh/Pettigo Peatlands, Ireland: Pinus sylvestris 0.983 0.887 Betula pubescens 0.989 0.921 Betula pendula 0.987 0.886 Empetrum nigrum 0.972 0.796 Formica lugubris 0.949 0.706 Eriophorum vaginatum 0.995 0.944 Quercus petraea 0.983 0.850 Listera cordata 0.971 0.816 Vaccinium myrtillus 0.988 0.903 Pteridium aquilinum 0.993 0.940 Calluna vulgaris 0.991 0.895 Rhynchospora alba 0.976 0.848 Carex bigelowii 0.953 0.697 Sphagnum cuspidatum 0.963 0.747 Vaccinium vitis-idaea 0.995 0.927 Trichophorum cespitosum 0.991 0.903

Once a network is trained, validated and tested at the European scale, it can then be used to produce a map of simulated suitable climate space for baseline (1961-90) climate and to estimate the potential re-distribution of this climate envelope under alternative climate change scenarios, at a finer 5km x 5km spatial resolution in Britain and Ireland.


3.3 Downscaled SPECIES model The SPECIES model has been downscaled to identify areas suitable for a given species at a finer resolution than in the purely climate-driven simulations (Pearson et al., 2004). This refined modelling approach is presented in Figure 3.1. The top half of the schematic presents the original SPECIES model, with continental-scale ANN training driven by climate at a broad spatial resolution of 0.5o latitude/longitude. The British-scale climate suitability surface is then incorporated, along with land cover classes, as input into a second ANN that is trained against British species distributions at a finer spatial resolution of 10km. This second ANN, which aims to characterise the relationship between species distribution, climate and land cover, has then been used to simulate regional scale suitability surfaces for species at 10 km and 1 km resolutions. Within the MONARCH 2 study, the downscaled ANN has been run using output climate suitability surfaces for Great Britain for those species associated with the Hampshire, Snowdonia and Central Highlands case studies, and output climate suitability surfaces for Ireland for those species associated with the Cuilcagh/Pettigo Peatlands case study. Land cover data for Great Britain are based on the CEH Land Cover Map 2000 and for Ireland on the Corine land cover map, which are both available at a 1km spatial resolution. Figure 3.1: Schematic of the downscaled SPECIES model (taken from Pearson et al., 2004). Tables 3.4 and 3.5 show the statistical performance of the regional trained neural network models at replicating the observed species’ distributions. For taxa other than birds, the Area Under the Receiver Operating Characteristic Curve (AUC) was used to assess the predictive performance of each network, as described for the European networks. Kappa values were not used at the regional scale because this statistic is affected by species prevalence such that rare species tend to result in very low kappa statistics that are not necessarily indicative of model performance. Alternatively, AUC is an unbiased measure of prediction accuracy, which is independent of both species prevalence and the decision threshold (Fielding and Bell, 1997).

Pre-processing: • Temperature indicators • Growing degree days • Soil water indicators

European scale climate database

Continental scale (SPECIES model): climate driven

GB scale climate database (with scenarios) Training using European

species’ distribution

GB climate suitability surface

GB land-cover classes (25 class system)

Training using GB species’ distribution

Regional scale: climate and land cover driven

Predicted species’ distribution


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For bird species, the comparative method of Fewster and Buckland (2001) was used which is particularly well-suited to highly mobile organisms, such as birds. The method works by calculating the ‘best attainable match’, after permitting mismatched squares to swap status with their near neighbours (within a given radius, here 30 km) until a maximum number of agreements between model and data, at the level of the single square, is produced. This coefficient, the ‘Best Attainable Match’ (BAM) thus takes account of the positions of matched and unmatched squares, as well as their number, and can then be used as an alternative means of locating an optimal threshold from the suitability surface. Calculations were carried out using the BAM software of Fewster and Buckland (2001). Table 3.4: Statistics showing predictive performance of the downscaled SPECIES model for birds, both in raw and kriged form. BAM = Best Attainable Match, SMC = Simple Matching Coefficient, the number of squares assigned to the correct status (see Fewster and Buckland, 2001).

Species Raw Atlas Data Kriged Atlas Data Threshold SMC (%) BAM (%) Threshold SMC (%) BAM (%) Numenius arquata 0.40 80.5 89.9 0.50 79.7 87.9 Pluvialis apricaria 0.50 88.0 92.7 0.45 88.0 94.4 Coccothraustes coccothraustes

0.25 88.4 95.0 0.20 90.2 93.8

Ficendula hypoleuca 0.30 81.7 92.5 0.40 83.1 92.2 Lagopus mutus 0.30 97.0 98.7 0.45 96.6 98.5 Parus montanus 0.45 84.7 94.8 0.55 82.8 93.1

Table 3.5: Statistics showing predictive performance of the downscaled SPECIES model for taxa other than birds.

Species AUC Species AUC Hampshire, England: Snowdonia, Wales: Fraxinus excelsior 0.971 Quercus petraea 0.740 Fagus sylvatica 0.913 Melampyrum pratense 0.761 Mercurialis perennis 0.875 Hyacinthoides non-scripta 0.897 Apodemus flavicollis (M) 0.823 Calluna vulgaris 0.890 Erica tetralix 0.914 Vaccinium myrtillus 0.931 Molinia caerulea 0.867 Carex bigelowii 0.957 Calluna vulgaris 0.890 Ulex gallii 0.868 Metrioptera brachyptera (I) 0.883 Pteridium aquilinum 0.913 Central Highlands, Scotland: Cuilcagh/Pettigo Peatlands, Ireland: Pinus sylvestris 0.949 Betula pubescens 0.784 Betula pendula 0.896 Empetrum nigrum 0.660 Formica lugubris (I) 0.747 Eriophorum vaginatum 0.569 Quercus petraea 0.740 Listera cordata 0.775 Vaccinium myrtillus 0.931 Pteridium aquilinum 0.734 Calluna vulgaris 0.890 Rhynchospora alba 0.807 Carex bigelowii 0.957 Sphagnum cuspidatum 0.513 Vaccinium vitis-idaea 0.921 Trichophorum cespitosum 0.826

M = Mammal I = Insect The results for bird species show occasional variation in the level of the optimal threshold, but remarkable consistency between the quality of the model fit at those thresholds, between the raw and kriged analyses. The Best Attainable Match ranges between 87.9% for Numenius arquata (curlew) and 98.5% for Lagopus mutus (ptarmigan) among the six species, compared to the simple proportions of correctly predicted cell statuses, which ranged from 79.7% to 96.6%. All subsequent analyses are based on the kriged data. Predicted distributions based on these data and a threshold as determined by the BAM coefficient in Table 3.4 reveal a strikingly accurate depiction of the British distributions for all six species.


Results for taxa other than birds show that 8 of the species modelled have AUC values greater than 0.9 indicating very good discrimination ability, whilst 16 species have values between 0.7 and 0.9 indicating reasonable discrimination ability and 3 species have values less than 0.7 indicating poor discrimination ability. The discrimination ability of the models is generally less at the regional scale than at the European scale, but this reflects the greater fragmentation, and sometimes greater rarity, of observed species’ distributions at finer spatial resolutions. Once a network is trained, validated and tested at the regional scale (on a 10km grid for Great Britain or Ireland), it can then be used to produce a map of the simulated distribution for baseline (1961-90) climate and land cover and to estimate the potential re-distribution of a species under alternative climate and land cover change scenarios at a finer 1km x 1km spatial resolution in each case study region. 3.3.1 Identifying decision thresholds To aid the interpretation and presentation of model results it is useful to identify a threshold value above which model outputs are considered to represent species presence. The choice of threshold value is important because model outputs, when mapped as presence/absence, may look quite different dependent on the threshold applied. A threshold is commonly identified by maximising the agreement between observed and simulated distributions. One approach is to use the threshold value that maximises the Kappa statistic of agreement, as described in the previous section (and applied in Pearson et al., 2002). An alternative approach, based on the ROC procedure, is to plot sensitivity against specificity (defined as the proportion of true negative predictions versus the number of actual negative sites) at a series of thresholds and to apply the threshold value at which these two curves cross (Figure 3.2, threshold a; Thuiller et al., 2003). In this way, the cost arising from an incorrect decision is balanced against the benefit gained from a correct prediction (Manel et al., 2001). Figure 3.2: Sensitivity and specificity plotted against threshold for defining decision thresholds. Threshold a is assigned at the point where the two curves cross. Thresholds b and c are defined by sensitivities of 90 and 95 % respectively (taken from Pearson et al., 2004). The above threshold values, based on maximising agreement between observed and simulated distributions, have been calculated for the European scale ANN results in the MONARCH 2 study. However, it may be argued that these approaches do not represent the most appropriate threshold for identifying those sites where a species could exist (i.e. the potential distribution). Given the many factors that influence the actual distribution of species, maximising the fit between simulated


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suitabilities and the observed distribution is likely to result in an underestimation of the extent of the potential distribution. In fact, we should be more concerned with minimising the number of sites with observed presences that are predicted to be unsuitable, than with minimising the number of sites without actual presences that are simulated as suitable. More formally, the primary concern when identifying a decision threshold should be to minimise the false negative fraction (the proportion of false negative predictions versus the number of actual positive sites). Minimising the false negative fraction is analogous to maximising sensitivity (since sensitivity = 1 – false negative fraction). It is therefore appropriate to define thresholds by assigning cut-off sensitivities as outlined in Figure 3.2. Cut-off values defined by sensitivity values of 90 and 95% (thresholds b and c in Figure 3.2) have been applied in MONARCH 2 for the regional scale ANN results. These thresholds are presented along with those delimited by balancing sensitivity and specificity, giving three levels of confidence in model simulations. 3.4 Land cover change scenarios A land cover model has been developed in order to create scenarios of land cover change that are consistent with the UKCIP02 climate change scenarios for application to the downscaled SPECIES model. The modelling procedure involved the development of a series of relationships between climate and soils variables (listed in Table A3.2) and the presence/absence of a land cover class. The contribution of the variables to the prediction of presence/absence of a land cover class was explored through the development of a generalised additive model using a logistic link function within Statistica 6.1 (Statsoft Inc, 2000). The parameters that were accepted and retained by the regression procedure were then used to generate predictive maps. The models are based on land cover data from the CEH Land Cover Map 2000 (LCM2000) for Great Britain and the Corine land cover map for Ireland. Baseline climate data covering the 30-year period from 1971 to 2001 were used to match the timescale over which the land cover data were collated. The modelling also made use of the soils datasets assembled for the project (see Chapter 1.3.1.3). Since the Scottish soils data were derived from a separate dataset and contained different variables, the land cover modelling was developed for England and Wales, Scotland and Ireland separately. The LCM2000 data used were the 27 subclasses (level 2 of the dataset), whilst for the Corine data 44 classes were available. Both datasets provide information on the percentage of a grid cell occupied by each land cover class and are available at a 1km resolution. However, the climate data for the modelling was only available at a 5km grid scale size, so all other data were aggregated to this level. The land cover class percentage coverage data were converted to presence/absence information using a cut-off threshold of 5%, above which a habitat was said to be present, to reduce the potential for errors resulting from the interpretation of the original satellite imagery. The base presence/absence data, identified in this manner, was then used to create the 5 x 5 km surfaces on which the models were based. The model was applied to all land cover classes that were considered to be impacted by climate change, but excluded land covers that were ubiquitous or where the baseline pattern of land cover data was considered to be a poor representation of reality. Table 3.6 lists the LCM2000 classes modelled for England and Wales and Scotland and the Corine classes modelled for Ireland. Classes excluded from the model for any of the reasons given above were still used as input to the downscaled SPECIES model, but their distributions under the climate change scenarios were not adjusted from their baseline values. The climate and land cover class data were randomly sampled and partitioned into training (70% of dataset) and validation (30% of dataset) subsets for use in the model development and validation analyses. Probability values were obtained for each respective land cover class and the threshold probability for presence of a land cover class was determined by maximising the agreement between the training and validation datasets. Interpretation of the predictive power of the models and the level of agreement between the training and validation sets was made using Cohen’s Kappa statistic of similarity (k) and the Area Under the Receiver Operating Characteristic Curve (AUC), which are described in Section 3.2.


Table 3.6: Land cover classes used in the modelling study from LCM2000 for England & Wales (E&W) and Scotland (S), and from Corine for Ireland.

LCM2000 classes Corine classes Code Description Code Description 8 Bog, deep peat (E&W; S) 12 Non-irrigated arable land

9 Dense dwarf shrub (E&W; S) 18 Pastures 10 Open dwarf shrub (E&W; S) 20 Complex cultivation patterns 11 Montane habitats (S) 21 Land principally occupied by agriculture 12 Broad leaved / mixed woodland (S) 23 Broad-leaved woodland 15 Neutral grass (E&W; S) 24 Coniferous forest 17 Bracken (E&W; S) 26 Natural grasslands 18 Calcareous grass (E&W; S) 27 Moors and heathland 19 Acid grassland (E&W; S) 29 Transitional woodland-shrub 20 Fen, marsh, swamp (E&W) 33 Burnt areas

35 Inland marshes 36 Peat bog

Figure 3.3 shows the pattern of representation of baseline land cover classes across each of the model areas. For every class the maximised kappa values, AUC values and percentage of squares correctly predicted by the model have been calculated (Tables 3.7 to 3.9) and an interpretation of the predictive power of the model made based on the Kappa statistic (Manel et al, 2001; Landis and Koch, 1977). The Receiver Operating Characteristic curves (Figures A3.4- A3.6 in the Annex to this Chapter) show that all three models perform well for most land cover classes. The models for Scotland have consistently lowest predictive power, whilst the England and Wales models are generally best. The lower predictive power of the Scottish datasets is probably a function both of the poorer soils datasets available and the patchiness of the baseline distributions of the land cover types. For Scotland, the soil information was related to depth and water content, whereas for England and Wales the variables also included information on clay content and humus content. There are also difficulties associated with modelling a finely mosaiced land cover distribution as occurs for most of the land cover types in Scotland, since the presence/absence pattern occurs at a finer resolution than was used for modelling.

For Ireland there is wide variation in the reliability of the models. Results from the Irish models for classes 21 (predominantly occupied by agriculture), 23 (broad leaved forest), 29 (transitional woodland shrub) and 35 (inland marshes) should be viewed with caution. Similarly the bracken class (17) and neutral grassland class (15) models have very low predictive power in Scotland.

Table 3.7: Validation statistics for land cover models constructed for Scotland.

Class Description Kappa AUC % Match Interpretation C8 Bog, deep peat 0.440 0.82 87.9 Moderate C9 Dense dwarf shrub 0.421 0.77 74.7 Moderate

C10 Open dwarf shrub 0.545 0.85 55.4 Moderate C11 Montane habitats 0.494 0.99 47.0 Moderate C12 Broad leaved / mixed woodland 0.387 0.80 66.4 Slight to fair C15 Neutral grass 0.229 0.71 66.2 Slight to fair C17 Bracken 0.181 0.79 43.4 Little or no agreementC18 Calcareous grass 0.404 0.88 82.6 Moderate C19 Acid grassland 0.346 0.73 66.9 Slight to fair


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Figure 3.3: Comparison of observed and predicted presence/absence data for acid grassland (England & Wales and Scotland) and moors and heathland (Ireland). Green = present. Purple = absent.

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Table 3.8: Validation statistics for land cover models constructed for England and Wales.

Class Description Kappa AUC % Match Interpretation C8 Bog, deep peat 0.589 0.90 93.8 Moderate C9 Dense dwarf shrub 0.590 0.87 73.9 Moderate

C10 Open dwarf shrub 0.664 0.89 86.1 Substantial C15 Neutral grass 0.314 0.71 20.6 Slight to fair C17 Bracken 0.576 0.91 58.0 Moderate C18 Calcareous grass 0.449 0.84 15.9 Moderate C19 Acid grassland 0.598 0.87 38.9 Moderate C20 Fen, marsh, swamp 0.418 0.85 48.1 Moderate

Table 3.9: Validation statistics for land cover models constructed for Ireland.

Class Description Kappa AUC % Match Interpretation 12 Non-irrigated arable land 0.741 0.94 77.0 Substantial 18 Pastures 0.500 0.93 83.8 Moderate 20 Complex cultivation patterns 0.488 0.83 66.6 Moderate 21 Land principally occupied by agriculture 0.280 0.68 56.1 Slight to fair 23 Broad-leaved forest 0.225 0.67 67.3 Slight to fair 24 Coniferous forest 0.370 0.75 58.3 Slight to fair 26 Natural grasslands 0.462 0.80 63.6 Moderate 27 Moors and heathland 0.509 0.83 70.2 Moderate 29 Transitional woodland-shrub 0.247 0.65 54.7 Slight to fair 33 Burnt areas 0.499 0.99 87.1 Moderate 35 Inland marshes 0.268 0.78 79.8 Slight to fair 36 Peat bogs 0.592 0.88 68.3 Moderate

3.5 Dispersal model The ability of species to track changes in the regional suitability surfaces simulated by the downscaled SPECIES model under the climate and land cover change scenarios will be dependent on the dispersal mechanisms by which migrations occur. The potential for species to migrate rapidly over large distances is a question of critical conservation importance as populations are increasingly threatened by climate change and the fragmentation of habitats (Sala et al., 2000; Hannah et al., 2002; Parmesan and Yohe, 2003). A spatially explicit cellular automata model has been developed to investigate the ability of species to migrate rapidly through fragmented landscapes (Pearson and Dawson, 2004). The model operates in discrete time and space and simulates stochastic dispersal at the landscape scale, with cell sizes of 10002 metres. As such, the dispersal kernel is designed to be flexible and to incorporate rare long-distance dispersal events, focusing on those few seeds that are expected to travel at least several hundred metres and thus drive migration at coarse spatial resolutions (Nathan et al., 2002). The model does not aim to simulate the fate of individual seeds, since this would be computationally impractical at the scale of analysis, but rather describes the dispersal of ‘propagules’, defined as the minimum number of individuals of a species capable of successfully colonising a new cell (Higgins et al., 2003a). The basic steps used in the model are set out in Figure 3.4. Having assigned species’ parameters and initialised the model with landscape suitabilities and initial populations, the model describes three basic steps: (i) survival, (ii) within-cell population dynamics, and (iii) dispersal. The survival step is analogous to mortality since any population falling on an unsuitable cell fails to survive. Cell suitability is defined as binary suitable or unsuitable in the current study, and suitabilities are changed across time steps according to the regional suitability surfaces produced by the downscaled SPECIES model for the UKCIP02 climate change scenarios and the combined climate and land cover change scenarios. Suitability is defined using the 95% cut-off threshold from the ROC curve described in Section 3.3.1.


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Within-cell population dynamics are incorporated to determine the number of propagules released by a populated cell in each time step. The model flow places dispersal properly within an organism’s life cycle, with the test for survival carried out before populations can release propagules (Hassell et al. 1995). Population growth is not initiated until the population in a cell has been established for a set period, defined by the number of time steps required for an individual plant to reach reproductive maturity. The number of propagules then released by a populated cell in each time step is determined by a combination of the inherent fecundity of the species (how many seeds does an individual plant produce?) and the size of the population. The likelihood of a population releasing propagules is assumed to increase with population size and population size is assumed to increase through time (except for extinction). A population density growth function is therefore incorporated within each cell, such that older populations have a higher probability of releasing propagules. Population growth is defined as following a Sigmoidal curve as the population rises from low density and saturates at the highest number permitted by the environmental resources. Such sigmoidal growth curves assume that population density is only affected by intraspecific competition (between individuals of the same species) and take no account of interspecific competition (between individuals of different species), yet have been observed in many natural situations (e.g. Alliende and Harper 1989). Figure 3.4: Flow diagram detailing the steps undertaken in the dispersal simulation (taken from Pearson and Dawson, 2004).


The model simulates the redistribution of the propagules according to a dispersal kernel. A dispersal kernel is a function describing the probability of a propagule dispersing to distance x from its source. The dispersal kernel included in the model is based on Clark et al. (1998) and can describe Gaussian, Exponential and ‘fat-tailed’ distributions (Figure 3.5). The ‘fat-tailed’ dispersal function enables low-probability long-distance dispersal events to be simulated. Long-distance dispersal (LDD) concerns that small percentage of seeds that travel significantly beyond the expected dispersal distance. Mechanisms by which LDD may be achieved are diverse and include the catching of seeds in updrafts, dispersal by birds in nest material and movement of seeds whilst attached to the fur of mammals (Higgins et al., 2003b). Evidence for LDD has been drawn from the palaeoecological record (Huntley and Birks, 1983; Davis and Shaw, 2001) and from contemporary observations, particularly of island colonisation and alien plant spread (Pitelka and Group, 1997; Clark, 1998; Higgins and Richardson, 1999; Cain et al., 2000; Horn, 2001; Gomez, 2003). The function is controlled by two parameters: a 'distance' parameter, which controls the mean distance a propagule can travel, and a 'shape' parameter (kurtosis, c) which controls the fatness of the tail. Figure 3.5: Dispersal kernels used in the model, after Clark et al. (1998). Each curve has the same mean and maximum dispersal distance, but a different amount of kurtosis. The high kurtosis of the fat-tailed kernel means that propagules will have a low, but not insignificant, probability of dispersing a long distance from the source. Plotting the probabilities on a log scale (inset) clarifies this potential for long-distance dispersal (taken from Pearson and Dawson, 2004).

0 5 10distance

p.d.

f.

‘Fat-tailed’: c = 0.5 (high kurtosis)

15

Exponential: c = 1.0

Gaussian: c = 2.0 (low kurtosis)

C = 0.5C = 1.0

C = 2.0}

The probabilistic nature of the dispersal process demands that the direction and distance of each dispersal event be selected through the generation of random numbers. Since we are simulating dispersal as a non-deterministic process, the model is run using a Monte Carlo approach. Hereby, the dispersal process is run many times so as to build up a probability surface identifying those cells more/less likely to be populated under certain dispersal assumptions.


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3.4.1 Application of the dispersal model to artificially fragmented landscapes An investigation of how different formulations of the dispersal kernel affect the ability of species to disperse through fragmented landscapes is reported in Pearson and Dawson (2004) based on artificial landscapes with differing amounts of suitable habitat cells and different patterns in the distribution of these cells. Illustrative results are shown in Figure 3.6 for a fat-tailed dispersal kernel with a mean and maximum dispersal distance of 1 and 15, respectively. Dispersal occurs across a two-dimensional grid of 100 x 100 cells with an artificially fragmented landscape containing 20% suitable habitat. Populations were initialised on suitable habitat in the centre four cells of the grid at time t = 0 and the model was run for 40 time steps and 10,000 Monte Carlo iterations. The probability of species dispersing within the patch of suitable habitat surrounding the initial populations is very high, whilst the probability that the species could disperse to the isolated regions of suitable habitat at the edges of the grid is much lower. These results demonstrate how the incorporation of long distance dispersal events within the model enable species to jump across patches of unsuitable habitat. Figure 3.6: Example of species dispersal through an artificially fragmented landscape with 20% suitable habitat showing the probability of dispersal on a log scale.

Log scale

0.0 000 2

1.00000Log scale:

1.0 0.0

3.4.2 Application of the dispersal model within the MONARCH 2 study The aim of the dispersal modelling work in MONARCH 2 was to ascertain whether species can track the changes in the suitability surface predicted by the downscaled SPECIES model for the UKCIP02 climate change scenarios for the 2020s and 2050s. The UKCIP02 scenarios for the 2020s are based on the average of the time period 2011 to 2040, whilst scenarios for the 2050s are based on the period 2041 to 2070. The mean point of these 30-year periods is 2025 and 2055. Hence, the dispersal model was run for 25 and 55 time steps for the 2020s and 2050s scenarios, respectively, assuming a base year of 2000. To allow the species time to react to the new climate and land cover suitability surfaces (for example by moving into new areas of suitable habitat), the scenario suitability surfaces are switched before 2025 and 2055. Specifically, the model was run with baseline suitability surfaces for 10 time steps then this was switched to the 2020s suitability surface and the model was run for a


further 15 time steps. At this point the dispersal probability map for the 2020s was saved. The model was then run for a further 30 time steps before the 2020s suitability surface was switched to the 2050s suitability surface and finally the model was run for 15 time steps before saving the dispersal probability results for the 2050s scenario. The model required parameterisation for six species-dependent variables before it could be applied within the four case study areas. These are maximum and mean dispersal distance, the shape parameter for the dispersal kernel, net reproductive rate, years to reach reproductive maturity and fecundity. Information on each variable was gathered from an extensive search of the ecological literature, supplemented by expert opinion. As specific information was rarely available, categories were defined to assist with the parameterisation of the model based on sensitivity analyses showing the implications of independent and combined variations in the main parameters. These categories were: • Shape of the dispersal kernel (see Figure 3.5):

Fat-tailed distribution (0.5) - birds, insects and herbaceous with light, wind-dispersed seeds Exponential distribution (1.0) - trees, very heavy seeds Gaussian distribution (2.0) - mammals

• Net reproductive rate:

Slow growth (1.5) - Perennials, trees and less than univoltine (one generation a year) organisms Medium growth (2.0) - Annuals, univoltine organisms Rapid growth (3.0) - Multi-voltine plants and organisms

• Fecundity:

5 categories, ranging from low (parameter value = 1) for species that produce few seeds, to high (value = 5) for species that produce many seeds.

Dispersal grids were created for the case study areas consisting of all 1km2 grid cells that fell into the area plus a buffer zone surrounding the case study of approximately 50% of its area in order to minimise effects caused by propagules dispersing off the edge of the grid. 3.5 Discussion and conclusions The complexity of the natural system presents fundamental limits to predictive modeling. The bioclimate envelope approach used in the SPECIES model can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of primary importance, and that model results should not be interpreted without due consideration of the limitations involved. There are important limitations to the predictive capacity of bioclimatic models, regardless of the methodology used to characterize the bioclimate envelope. Three of the main criticisms of the bioclimatic approach are biotic interactions, evolutionary change and species dispersal (Pearson and Dawson, 2003). The latter criticism has been addressed in the MONARCH 2 project by coupling the modelled species’ suitability surfaces resulting from the downscaled SPECIES model with dynamic simulations of species dispersal. The importance of biotic interactions between species, such as competition, predation and symbiosis with other species, have been shown to have important impacts on species distributions. Changes to the distribution of a single species could have significant knock-on impacts on the distributions of many other species. It is thus apparent that modelling strategies based on bioclimate envelopes alone may in some cases lead to predicted distributions that are, in fact, wildly incorrect. However, it is argued that applying bioclimatic models at macro-scales, where climatic influences on species distributions are shown to be dominant, can minimize the impact of biotic interactions (Pearson and Dawson, 2003). The implications of rapid evolutionary change for bioclimate envelope modelling are important since the assumption of niche conservatism, whereby rates of adaptation are slower than extinction rates, will be wrong for species experiencing sufficiently


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rapid adaptation. Predicting adaptive changes to species in response to climate change presents a huge challenge to vegetation modellers and has not been accounted for within the MONARCH2 modelling framework. It is thus apparent that applications of bioclimate envelope models for predicting distribution changes over the next century are most appropriate for species not expected to be able to undergo rapid evolutionary change over this timescale. This is most likely to be the case for long-lived species and poor dispersers, since intergenerational selection and/or selection at expanding range margins is required for evolutionary processes to take effect (Pearson and Dawson, 2003). The bioclimate envelope approach used in the SPECIES and downscaled SPECIES models is based on Artificial Neural Networks (ANNs). These have increasingly been employed in ecological studies as an alternative to more traditional statistical techniques (Lek and Guegan, 1999). The advantages and disadvantages of using ANNs for characterising species distributions have been discussed in detail by Hilbert and Ostendorf (2001) and Pearson et al. (2002). Of particular note here is the ability of ANNs to identify non-linear responses to environmental variables and to incorporate multiple types of input variables, including categorical (e.g. land cover classes) and non-categorical (e.g. climate suitability) data. A notable disadvantage is that the relative contribution of different input variables is not immediately identified in an ANN, though further analysis of the network can increase the explanatory power of the approach (Gevrey et al., 2003). The interaction between climate and habitat availability plays an important role in determining the biogeography of species. The downscaled SPECIES model was developed to enable the combined effects of climate and land cover change on individual species to be studied and to help uncouple effects of climate and habitat change in the interpretation and prediction of species’ distribution. The model defines the relationship between climate, land cover and species’ distributions at a 10km2 spatial resolution before applying these relationships at a 1km2 resolution. The presence/absence of land cover types at a 10km2 resolution does not always provide a good correlate with species’ distributions. This is due to the fact that at this resolution nearly all 10km2

cells incorporate at least a small patch of suitable land cover (i.e. a ‘presence’), leading to blanket coverage throughout the study region. In order to better identify correlations between land cover type and species’ distributions it would be necessary to adopt a finer resolution of analysis at which patterns in the distribution of suitable land cover are apparent in the dataset. This was not possible in the MONARCH 2 study because 10km2 was the finest resolution at which observed species distributions were available for Britain and Ireland. Whilst the inclusion of land cover in the downscaled SPECIES model was able to improve the simulation of current species’ distributions at the national scale in many cases, the statistics describing the discrimination ability of the models were lower than those based on climate alone, which are derived at the European scale. Furthermore, the testing of the downscaled SPECIES model under the climate and land cover change scenarios (see Chapters 6 to 9) showed that the signal from the impact of climate change (i.e. gains and losses in climate space) was suppressed in some predictions when compared with outputs from the original SPECIES model. This is likely to be a result of the architecture of the ANN, which consisted of 22 input nodes related to land cover classes for Great Britain (and 38 for Ireland) but only 1 input node related to the climate suitability surface. A less complex approach to the integration of land cover constraints to species’ distributions, utilising simple land-cover ‘masks’, may have been more informative and is recommended for future research. It is important to note that the land cover model results only represent projections in the location of the climate envelopes that are currently occupied by these land cover classes. It does not consider any economic or social drivers that may affect the location or management of such land cover types. As illustrated in projects such as RegIS, for certain land cover types, such as those dominated by agricultural or forestry production, it is clear that land use and agricultural policy will have as great an influence on the future location of such land cover types as climate (Holman and Loveland, 2002). The model proved most successful where the climate and soil conditions provided strong restrictions on the locations of land cover types (e.g. for peat or calcareous based land covers). All results for classes showing a poor agreement between the training and validation datasets should be treated with caution. There is a need to extend the range of climatic conditions over which the land cover model


was developed using European land cover data in order to capture the full range of class types that may exist under future climatic conditions. This would require the modelling to rely solely on the Corine dataset, since it is the only one that extends beyond Britain and Ireland. Ideally this wider modelling exercise would also include some measure of socio-economic indicators as covariates within the model in order to capture the non-climatic elements of the baseline distributions. A number of limitations to the modelling approach have been noted, including biotic interactions, evolutionary change, the restricted explanatory power of ANNs and the reliance on correlations between observed distributions and environmental variables. Further limitations are inherent in the availability and accuracy of datasets. Data can rarely be generated for all resolutions and for all spatial extents, but rather tends to be available for large extents at coarse resolutions, or small extents at fine resolutions. Thus, in the MONARCH 2 study species distributions were obtained at 0.5o

resolution for Europe, 10 km2 resolution for Britain and Ireland, and 1 km2 resolution for the local case studies. It has been necessary to design the modelling framework to take best advantage of the available data. Questions regarding the accuracy of the data also arise, in particular regarding the assumption that observed species absences are true absences, and not a result of insufficient sampling (Griffiths et al., 1999). The use of species records spanning many years is also potentially problematic, since distributions are dynamic over relatively short time-scales. The use of mean 1961-90 climate data aims to reduce this effect, though the single year (1998) ‘snapshot’ of land cover will add an element of error to the modelling. Base errors arising from data limitations are unavoidable. However, the level of success that has been achieved in modelling species distributions has demonstrated that biogeographical trends can be identified regardless of the imperfect data that is so often all that is available in ecological studies. Differences between decision thresholds must be considered when interpreting presence/absence maps generated from such models. It has been argued here that, rather than maximising agreement between observed and simulated distributions, a more appropriate approach to identifying decision thresholds is to minimise the number of observed presences falling outside the simulated distribution. The three-level approach to presenting model output applied in this study makes the interpretation of results less dependent on the choice of a single threshold and facilitates the identification of broader potential distributions. These broader potential distributions based on the 95% cut-off threshold provided the regional scale suitability surfaces for coupling with dynamic simulations of species dispersal. The incorporation of long distance dispersal (LDD) within the dispersal model enables investigation of the potential for species to migrate rapidly under future climate change. However, identifying which plants are most likely to disperse via LDD in the future is problematic, particularly given that we can expect long-distance events to be caused by non-standard means of dispersal (Higgins et al., 2003b), so making the categorisation of species as more or less likely to disperse difficult. We can be certain, however, that different dispersal mechanisms will result in very different abilities of species to keep track of changing climate regimes (Collingham et al., 1996; Collingham and Huntley, 2000), which will have important consequences for the future composition and functioning of ecological communities (Berry et al., 2002; Pearson and Dawson, 2003). The great complexity of natural systems suggests that there are fundamental limits to the prediction of future species’ distributions. Combining the complexities arising from biotic interactions, evolutionary change, modelling approach and data accuracy, along with the uncertainties all too evident in predictions of future climate and land cover change, it is apparent that accurate predictions of future species distributions are not currently possible (Pearson and Dawson, 2003). The development of dynamic global vegetation models (DGVMs), which include mechanistic representations of physiological, biophysical and biogeochemical processes, has demonstrated significant progress in the modelling of vegetation–climate interactions at the global scale (Woodward and Beerling, 1997; Cramer et al., 2001). Recent development of these techniques for application at regional scales, including the breaking down of ecosystem processes into key components with characteristic spatial and temporal scales shows much promise (Sykes et al., 2001). However, the


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complexity of DGVMs makes their parameterisation and validation problematic, and does not currently allow their widespread application to specific species and regions (Pearson and Dawson, 2003). Alternatively, the relatively simple SPECIES bioclimate envelope model linked in a scale-dependent hierarchical manner with land cover data and a dynamic model of species dispersal can provide a useful starting point when applied to suitable species and at appropriate spatial scales. The importance of the model predictions undertaken in the MONARCH 2 project should not be underestimated, though model predictions should be interpreted with due caution and should be viewed as first approximations indicating the potential magnitude and broad pattern of future impacts, rather than as accurate simulations of future species distributions. 3.6 References Alliende, M.C. and Harper, J.L. (1989). Demographic studies of a dioecious tree. I. Colonisation, sex and age-structure of a population of Salix cinerea. Journal of Ecology, 77, 1029-1047. Berry, P.M., Dawson, T.P., Harrison, P.A, Pearson, R.G. and Butt, N. (2003). The sensitivity and vulnerability of terrestrial habitats and species in Britain and Ireland to climate change. Journal for Nature Conservation, 11, 15-23. Berry P.M., Dawson T.P., Harrison P.A. and Pearson R.G. (2002). Modelling potential impacts of climate change on the bioclimatic envelope of species in Britain and Ireland. Global Ecology and Biogeography, 11, 453-462. Berry, P.M., Vanhinsbergh, D., Viles, H.A., Harrison, P.A., Pearson, R.G., Fuller, R., Butt, N. and Miller, F. (2001). Impacts on terrestrial environments. In: Harrison, P.A., Berry, P.M. and Dawson, T.P. (Eds.) Climate Change and Nature Conservation in the Britain and Ireland: Modelling natural resource responses to climate change (the MONARCH project). UKCIP Technical Report, Oxford. Cain M.L., Milligan B.G. and Strand A.E. (2000). Long-distance dispersal in plant populations. American Journal of Botany, 87, 1217-1227. Clark J.S. (1998). Why trees migrate so fast: confronting theory with dispersal biology and the Paleorecord. The American Naturalist, 152, 204-224. Clark J.S., Fastie C., Hurtt G., Jackson S.T., Johnson C., King G.A., Lewis M., Lynch J., Pacala S., Prentice C., Schupp E.W., Webb III T. and Wyckoff P. (1998). Reid's paradox of rapid plant migration: dispersal theory and interpretation of paleoecological records. BioScience, 48, 13-24. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46. Collingham Y.C., Hill M.O. and Huntley B. (1996). The migration of sessile organisms: a simulation model with measurable parameters. Journal of Vegetation Science, 7, 831-846. Collingham Y.C. and Huntley B. (2000). Impacts of habitat fragmentation and patch size upon migration rates. Ecological Applications, 10, 131-144. Collingham, Y.C., Wadsworth, R.A., Huntley, B. and Hulme, P.E. (2000). Predicting the spatial distribution of non-indigenous riparian weeds: issues of spatial scale and extent. Journal of Applied Ecology, 37, 13-27. Cramer, W., Bondeau, A., Woodward, F.I., Prentice, I.C., Betts, R.A., Brovkin, V., Cox, P.M., Fisher, V., Foley, J.A., Friend, A.D., Kucharik, C., Lomas, M.R., Ramankutty, N., Sitch, S., Smith, B., White, A. and Young-Molling, C. (2001). Global response of terrestrial ecosystem structure and


function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology, 7, 357–373. Davis M.B. and Shaw R.G. (2001). Range shifts and adaptive responses to Quaternary climate change. Science, 292, 673-679. Fewster, R.M. and Buckland, S.T. (2001). Similarity indices for spatial ecological data. Biometrics, 57, 495-501. Fielding, A. H. and Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38-49. Franklin, J. (1995). Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19, 474-499. Gevrey, M., Ioannis, D. and Lek, S. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160, 249-264. Gomez J.M. (2003). Spatial patterns in long-distance dispersal of Quercus ilex acorns by jays in a heterogeneous landscape. Ecography, 26, 573-584. Griffiths, G. H., Eversham, B. C. and Roy, D. B. (1999). Integrating species and habitat data for nature conservation in Great Britain: data sources and methods. Global Ecology and Biogeography, 8, 329-345. Guisan, A. and Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147-186. Hannah L., Midgley G.F. and Millar D. (2002). Climate change-integrated conservation strategies. Global Ecology and Biogeography, 11, 485-495. Harrison, P.A., Vanhinsbergh, D.P., Fuller, R.J. and Berry, P.M. (2003). Modelling climate change impacts on the distribution of breeding birds in Britain and Ireland. Journal for Nature Conservation, 11, 31-42. Harrison, P.A., Berry, P.M. and Dawson, T.P. (Eds.) (2001) Climate Change and Nature Conservation in the Britain and Ireland: Modelling natural resource responses to climate change (the MONARCH project). UKCIP Technical Report, Oxford. Hassell, M.P., Miramontes, O., Rohani, P. and May, R.M. (1995). Appropriate formulations for dispersal ability in spatially structured models: comments on Bascompte and Sole. Journal of Animal Ecology, 64, 662-664. Higgins, S.I., Lavorel, S. and Tackenberg, O. (2003a). Plant dispersal and habitat loss synergies. In: Climate Change and Biodiversity: synergistic impacts (eds. Hannah, L. and Lovejoy, T.E.), pp. 71-76. Conservation International, Washington. Higgins K. and Richardson D.M. (1999). Predicting plant migration rates in a changing world: the role of long-distance dispersal. The American Naturalist, 153, 464-475. Higgins, S.I., Nathan, R. and Cain, M.L. (2003b). Are long-distance dispersal events in plants usually caused by non-standard means of dispersal? Ecology, 84, 1945-1956. Hilbert, D. W. and Ostendorf, B. (2001). The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecological Modelling, 146, 311-327.


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Holman, I and Loveland, P (2002). Regional climate change impact and response studies in East Anglia and North West England (RegIS). CC0337, Final report to MAFF, Soil survey and Land Research Centre, Cranfield. Horn H.S. (2001). Long-distance dispersal of tree seeds by wind. Ecological Research, 16, 877-885. Huntley B. and Birks H.J.B. (1983). An atlas of past and present pollen maps for Europe: 0-13,000 B.P. Cambridge University Press, Cambridge. Landis, J.R, and Koch, G.G. (1997). The measurements of observer agreement for categorical data. Biometrics, 33(1), 159-174. Lek, S. and Guegan, J. F. (1999). Artificial neural networks as a tool in ecological modelling: an introduction. Ecological Modelling, 120, 65-73. Manel, S., Williams, H. C. and Ormerod, S. J. (2001). Evaluating presences-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38, 921-931. Nathan R., Katul G.G., Horn H.S., Thomas S.M., Oren R., Avissar R., Pacala S.W. and Levin S. (2002). Mechanisms of long-distance dispersal of seeds by wind. Nature, 418, 409-413. Parmesan, C. and Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37-42. Pearson, R.G. and Dawson, T.P. (2004). Long-distance plant dispersal and habitat fragmentation: identifying conservation targets for landscape planning under climate change. Biological Conservation, in review. Pearson, R. G. and Dawson, T. P. (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12, 361-371. Pearson, R.G., Dawson, T.P. and Lui, C. (2004). Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography, 27, 285-298.. Pearson, R.G., Dawson, T.P., Berry, P.M. and Harrison, P.A. (2002). SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecological Modelling, 154, 289–300. Pitelka L.F. and Group P.M.W. (1997). Plant migration and climate change. American Scientist, 85, 464-473. Sala, O. E., Chapin III, F. S., Armesto, J. J., Berlow, E., Bloomfiled, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Sykes, M. T., Walker, B. H., Walker, M. and Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. Science, 287, 1770-1774. StatSoft, Inc (2001). STATISTICA for Windows. Tulsa, OK, StatSoft, Inc. Sykes, M.T., Prentice, I.C., Smith, B., Cramer, W. and Venevsky, S. (2001). An introduction to the European Terrestrial Ecosystem Modelling Activity. Global Ecology and Biogeography, 10, 581–593. Thuiller, W., Vaydera, J., Pino, J., Sabate, S., Lavorel, S. and Garcia, C. (2003). Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain). Global Ecology and Biogeography, in press. Travis, J. M. J. (2003). Climate change and habitat destruction: a deadly anthropogenic cocktail. Proceedings of the Royal Society of London, Series B, 270, 467-473.


Woodward, F.I. and Beerling, D.J. (1997) The dynamics of vegetation change: health warnings for equilibrium ‘dodo’ models. Global Ecology Biogeography Letters, 6, 413–418.

MONARCH 2 Report – Chapter 3 Annex ___________________________________________________________________________

63

Chapter 3 Annex Technical details of land cover modelling A3.1 Model derivation Table A3.1 indicates the range of additional climate variables that were calculated from the UKCIP02 data. Some of these variables were also used in the bioclimatic classification (see Chapter 2). Table A3.2 lists the combined climate and soils datasets that were initially entered into the modelling phase for each country/country group. Table A3.1: Calculated parameters for the UKCIP02 data.

Parameter Description GDD Sum of Growing degree days above 5.0oC (used in UKCIP98 – 10km) GDD04 Sum of Growing degree days above 4.0 oC (used in UKCIP02 baseline – 5km) Abs Tmin Calculated using Prentice et al. (1992) as the absolute minimum temperature of the

coldest month over a 20 year period StartGS Starting date of growing season assuming the start is the 10th consecutive day with

temperatures above 5.0oC (UKCIP98 10km method) StartGS02 Starting date of growing season assuming the start is the 5th consecutive day with

temperatures above 5.0 oC (UKCIP02 5km method) EndGS End date of growing season assuming the end is the 5th consecutive day with temps

below 5.0 oC (used in both UKCIP98 and UKCIP02 data) LengthGS Number of days starting on the 10th consecutive day with temperatures above 5.0oC and

ending on the 5th consecutive day with temps below 5.0 oC (used in UKCIP98 data) LengthGS02 Number of days starting on the 5th consecutive day with temperatures above 5.0 oC and

ending on the 5th consecutive day with temps below 5.0 oC (used in UKCIP02 baseline) The contribution of the variables to the prediction of presence/absence of a land cover class was explored through the development of a generalised additive model using a logistic link function (Equation 1) within Statistica 6.1 (Statsoft Inc, 2000). Parameters were entered in a backward stepwise manner, with a maximum number of iterations set to 100, significance of entry and removal set to 0.05, sweep delta set to 1E-7, convergence at 1E-7 with sigma restricted estimated. The parameters that were accepted and retained by the regression procedure were then used to generate predictive maps using equation 2. Equation 1

∑=

+=−

=27

111

log)(logit i

iio xbbp

pp

Equation 2

xm)bm...x1b1exp(b01xm)bm...x1b1exp(b0y•++•++

•++•+=


64

Table A3.2: Modelled parameters included in the land cover class model.

Country England and Wales Scotland Ireland Soil Parameters

CALCPERC PEATPERC HUMOSEPERC SBPEATPERC GLEYGWPERC GLEYSWPERC

TAWC1 SS_TAWC DEPTH1

SoilAWC

Climatic Parameters

Annual Rainfall Total Summer Rainfall Total Winter Rainfall Total Annual Tmean Summer Tmean Summer Tmin Winter Tmean Winter Tmin Summer Tmax Winter Tmax Winter PET Summer PET GDD>5°C GDD>4°C Abs Tmin StartGS StartGS02 EndGS

Annual Rainfall Total Summer Rainfall Total Winter Rainfall Total Annual Tmean Summer Tmean Summer Tmin Winter Tmean Winter Tmin Summer Tmax Winter Tmax Annual PET Winter PET Summer PET GDD>5°C GDD>4°C Abs Tmin StartGS StartGS02 EndGS LengthGS LengthGS02; LengthGS02;

Annual Rainfall Total Summer Rainfall Total Winter Rainfall Total Annual Tmean Summer Tmean Summer Tmin Winter Tmean Winter Tmin Summer Tmax Winter Tmax Winter Radiation Summer Radiation Annual Radiation Annual Wind Winter Wind Summer Wind GDD>5°C GDD>4°C Abs Tmin StartGS StartGS02 EndGS LengthGS LengthGS02;

A3.2 Results Figures A3.1 to A3.3 show an interpretation of the predictive power of the models based on the kappa threshold (after Manel et al., 2001). The interpretation from these land cover class graphs shows that for some land classes there is only slight agreement between the predicted and raw data. However, analysis of agreement using ROC curves (Figures A3.4 to A3.6), indicate that the model has performed well across most of the land classes. It was concluded, therefore, that although the kappa values provide an indication of a good threshold, a better threshold might be obtained by further analysis of the ROC curves.


65

Figure A3.1: Kappa statistics for Scotland.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

C8C9

C10C15C17C18C19C20

Lan

d cl

ass

Kappa value

Kappa interpretation 0-0.2 Little or no agreement 0.2-0.4 Slight to fair 0.4-0.6 Moderate 0.6-0.8 Substantial 0.8-1.0 Almost total agreement

Figure A3.2: Kappa statistics for England and Wales

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

C8C9

C10C11C12C15C17C18C19

Lan

d cl

ass

Kappa value


Figure A3.3: Kappa statistics for Ireland

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

121820212324262729303132333536

Lan

d cl

ass

Kappa value



66

Figure A3.4: Receiver operating characteristic curves for the Scottish land cover classes.

Scotland

1-Specificity

Sen

sitiv

ity

Landclass: C8

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C9

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C10

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C11

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C12

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C15

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C17

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C18

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C19

0.0 0.2 0.4 0.6 0.8 1.0

Figure A3.5: Receiver operating characteristic curves for the England and Wales land cover classes.

1-Specificity

Sen

sitiv

ity

Landclass: C8

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C9

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C10

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C15

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C17

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C18

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C19

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C20

0.0 0.2 0.4 0.6 0.8 1.0


67

Figure A3.6: Receiver operating characteristic curves for the Irish land cover classes.

Ireland

1-Specificity

Sen

sitiv

ity

Landclass: C12

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C18

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C20

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C21

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C23

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C24

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C26

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C27

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C29

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C30

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C31

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C32

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C33

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Landclass: C35

0.0 0.2 0.4 0.6 0.8 1.0

Landclass: C36

0.0 0.2 0.4 0.6 0.8 1.0


4 Implications for the Composition of Species Communities G.J. MASTERS AND N.L. WARD Summary One of the limitations of the climate envelop, land cover and dispersal models developed for MONARCH 2 was their inability to take into consideration inter-specific competition and interactions (see Chapter 3). In an attempt to address this issue an assessment of species interactions (type and strength) was undertaken to estimate habitat level impacts of simulated distribution changes. Of particular concern were the consequences of species arriving (through dispersal) or leaving (through local extinction) due to climate change within the selected habitats of each case study area. An interaction matrix was developed based on ecological knowledge of all the selected species and those with which they are understood to interact. This informed the conceptual models by enabling an assessment of the likely ecological impacts of the loss or arrival of the species. The predictions from the downscaled SPECIES and dispersal modelling were examined using the Leaver conceptual model if a species was predicted to be lost within the case study area or using the Arriver model if it was predicted to disperse within the case study area. The conceptual modelling provided an assessment of how communities/habitats may respond to a particular species’ response to climate change, particularly on community composition. The conceptual models could not be applied if no change in species distribution was predicted. 4.1 Introduction Changes in species composition, abundance, species richness, diversity and/or functional type affect the paths and efficiency with which resources are processed within an ecosystem and thus affect function. However, the conceptual framework developed in this chapter was based on species and their interactions by creating a descriptive species interaction matrix and Arriver and Leaver models. Classically, this approach can be considered as community composition and dynamics or by the relatively new term of metacommunities (Leibold et al., 2004). Extinction is often a global phenomenon but can also refer to loss from a country, region, habitat or community. Throughout this chapter, local extinction is used to describe the natural loss of a species from a habitat or community. With climate change, species are expected to move from or become extinct from existing habitats/communities when the changing environmental conditions (or their consequences) begin to become intolerable for co-existence. However, as is shown for the extension in range northwards from southern refugia after the last ice age (during a period of warming), all the organisms within a community/ecosystem do not move together as a unit. Instead, species showed a large degree of individualism in their timing, rate and direction of response (Huntley, 1991). Communities will therefore undergo change with the loss of species and the addition of new species so that new species assemblages will be created. This implies that there will be some degree of community assembly, disassembly and reassembly. The impact of species loss and addition in terms of changes in species richness, functional richness and community composition are of crucial importance when attempting to model and predict the effects of climate change on a habitat or community. 4.2 The species Arriver and Leaver conceptual models These models were used to examine the community level impact of a species arriving or a species leaving. There are obviously other alterations to the habitat that were not included in MONARCH 2 models but are recognised in the discussions within each case study chapter.


69

A species interaction matrix informed these two conceptual models. Such an approach was based on the following assumptions (supported by the literature): • Changes in species composition, abundance, richness, and/or functional type are inter-related; • A community’s response to changes in biodiversity may depend on its composition, i.e. which

functional types and species are lost and which remain; • Some species matter more than others: loss of a species can have a disproportionate impact on the

community (e.g., loss of a keystone or dominant species); • At least one species per functional group is essential for the persistence (stability) of a species

community, more than one species per functional group may insure against community collapse in times of disturbance or environmental change.

4.2.1 The species interaction matrix To inform the modelling process an extensive literature review was conducted to identify information regarding the effects of species arrival or departure from communities or habitats. Additionally, an extensive literature review was conducted to identify the species relations from which the species interaction matrix was developed. This was obviously different and developed separately for each of the selected species and habitats. A generic species interaction matrix is shown in Figure 4.1 to show the general format and specific examples can be found in Chapters 6-9. Figure 4.1: Interactive matrix of a community. The matrix consists of a generalised food web combined with species interaction direction and strength. All species interact directly or indirectly with each other. Only direct interactions are illustrated for clarity.

P = plant/ producerH = herbivorePr = predatorC = communitorsSO = soil organismsS prefix = specialistG prefix = generalistCircle size represent relative abundanceArrow thickness represents interaction strength

C P1 P2 C

Nutrient availability

GPr

GPr

GPr

GH

SPrSPr

SH SH

SSO SSOGSO


The species interaction model (Figure 4.1) informs two conceptual models detailing community response pathways for two situations: (i) the Leaver model, which examines the consequences of a species leaving a community

(Figure 4.2) and; (ii) the Arriver model, which examines the consequences of a species arriving in a community

(Figure 4.3). In both models species are classified as being dominant, sub-dominant or rare (defined below). These species categories were also, in part, a component of the species selection criteria (see Chapter 2.7.1.2). Keystone species (defined below) are also considered for completeness but, in relation to the case study areas, it is difficult to identify true keystone species (as opposed to simply identifying a top predator, for example) without experimental manipulation of the communities being considered so they were not identified within the case studies. Keystone status is a functional attribute distinct from the others that are related to species abundance. Dominant species: those species that are characteristic of each habitat and so are generally the more abundant or frequent species within the habitat, to a large extent governing the type and abundance of other species in the community (after Greig-Smith, 1983; Tilman, 1982). Sub-dominant species: those species that are common but not as frequent/abundant as dominant species. These species reflect the middle ground between scarce/rare species and the dominants. Generally, these species are often important for community persistence and stability. Their position within the community is probably a result of interactions with other species, such as the dominant(s) (after Tilman, 1982). Rare species: although these species can often be characteristic of a particular species community they are of low abundance. This does not mean that they are of low importance, as in aggregate they contribute (if not determine) the diversity and species richness of a community as a whole (after Odum, 1989). Keystone species: those species that have a major influence on the structure and composition of an ecosystem or community. Its presence impacts many other members of the community, disproportionate to its abundance within the community, and if it becomes extinct from the community, there can be far-reaching consequences for the habitat, generally through initiating changes in community structure and composition (often a loss of diversity) (after Paine, 1966; 1969). The models recognise that communities change with time, so a once rare species, can, with a disturbance or simply with enough time, become a dominant species (Figures 4.2 and 4.3). 4.2.1.1 The Leaver model Figure 4.2 shows that for a species to have a significant impact on community composition, through causing community reassembly, then the departing species needs to be a dominant species (a driver of the ecosystem), a keystone species (if one can be identified) or come from a functional group where there is no functional type redundancy (the species is the sole representative of that particular functional type). Functional type redundancy occurs when there is more than one species as members of a single functional group, i.e. replication of species of the same functional type (after Odum, 1989). If the species that leaves does not meet any of these requirements (with conservative estimates for keystone species) then the effect of its departure on the ecosystem will be negligible or slight. Even so, with time a very small immediate effect on species composition can lead to a new community forming by altering the long-term natural successional trajectory.


71

Figure 4.2: The Leaver model. This conceptual model explores the consequences of a species leaving a community due to climate change. The model recognises that communities are dynamic entities changing over time (e.g. succession) and that climate change is going to be a major disturbance on community structure, function and dynamics.

Sub-dominantSpeciesLeaver

Community collapse

Reassembly

New Community

Existing CommunityNegligible effect

Functional TypeRedundancy?

Keystone species?

Major effect

Yes

No

No

Yes

Natural

change,

e.g. su

ccessio

n

Dominant Species• strong links with other species• driver of belowground processes

Rare Species• weak interaction strength• often dependent on biotic interactions

4.2.1.2 The Arriver model The consequences of a species arriving in a new community or habitat are more complex than for a species leaving (Figure 4.3). The propagule pressure (number of propagules, e.g. seeds, of a particular species entering a habitat or community) for the arriver will determine the probability of recruitment and establishment within the ecosystem. Local extinction of the arriving species can occur during either recruitment or establishment, but the probability of extinction is greater with less propagule pressure. Once the species has established it can be considered a coloniser. The consequences for community composition depend on what the coloniser’s status becomes. If the coloniser invades and expands to become a dominant species or represents a new functional type/keystone species, then there will a large impact on community composition, with probably some form of community collapse and reassembly leading to a highly modified community.


Figure 4.3: The Arriver model. This conceptual model explores the consequences of a species arriving in a community due to climate change. The model recognises that communities are dynamic entities changing over time (e.g. succession) and that climate change is going to be a major disturbance on community structure, function and dynamics.

Reassembly

Highly modified

community

Community collapse

Natural change,

e.g. su

ccessio

n

Existing community

No impact

Large impact

Arr

iver

4.2.2 Model application and links with SPECIES and dispersal modelling If species within the case study areas were predicted to redistribute outside that area then the Leaver conceptual model was applied while the Arriver model was applied if species were predicted to spread into the case study area. The interaction matrix was used to assess possible impacts of each species' movement on the community. The models could not be applied if there was no change in species' distribution predicted per test area, but inferences on possible community impacts due to climate change could still be made using the interaction matrix and the literature. 4.3 The application of the Arriver and Leaver conceptual models For application to the case study areas, an interactive matrix for each of the species selected for each of the habitats within the case study area was constructed from primarily the scientific literature, and if required, from the grey literature. One criterion for building the species relations shown in the interactive matrices was a supporting reference. The downscaled SPECIES and dispersal modelling results (Chapter 3) informed the species interaction matrices if a selected species was predicted to leave a selected habitat or if a selected recruitment species would arrive in a selected habitat. The relevant conceptual model (Arriver or Leaver) was applied to the relevant species interaction matrix and the consequences for the selected habitat (community) of a species arriving or leaving predicted. Four scenarios were developed to predict the effects on the ecosystem of a species leaving. These involved applying the Leaver model (Figure 4.2) to the interactive matrix for the following scenarios: loss of a dominant species, loss of a rare species, loss of a functional type (with no functional

Coloniser

Establi

shmen

t

Recru

itmen

t

Extinct

Rare SpeciesNegligible effect

Dominant Species

Expansion

Keystone species

NewFunctional Type

ExistingFunctionalType

Sub-dominantSpecies


73

redundancy) and loss of a functional type (with functional redundancy). Similar approaches can be applied to the Arriver model. The results for the selected species are presented within Chapters 6-8. 4.4 Discussion The development of the Arriver and Leaver conceptual models and the interactive species matrix approach that underpins the models goes some way to understanding community level impacts. However, the conceptual models and the interactive matrices can only explore scenarios, they are very difficult to quantify, even if the data existed to enable quantification. Assembling a full interactive matrix for each case study was limited by lack of data on all species present; some groups are covered relatively well while others are missing from local datasets. All the information gathered on species had to be supported; hence the emphasis on the scientific literature as a source, but data was limited by the scope of publishing. Nevertheless, changes in community composition and complexity can be predicted from the application of these models. 4.5 References Greig-Smith, P. (1983). Quantitative Plant Ecology (3rd edn.). Blackwell Scientific Publications, Oxford. Huntley, B. (1991). How plants respond to climate change: migration rates, individualism and the consequences for plant communities. Annals of Botany (Supplement 1), 67, 15-22. Leibold, M.A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J.M., Hoopes, M.F., Holt, R.D., Shurin, J.B., Law, R., Tilman, D., Loreau, M. and Gonzalez, A. (2004). The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters, 7, 601-613. Odum, E.P. (1989). Ecology and our endangered life-support systems. Sinauer Associates, Massachusetts. Paine, R.T. (1966). Food web complexity and species diversity. American Naturalist, 100, 65-75. Paine, R.T. (1969). A note on trophic complexity and community stability. American Naturalist, 103, 91-93. Tilman, D. (1982). Resource competition and community structure. Monographs in population biology, 17. Princeton University Press, New Jersey.


5 Impacts on coastal environments

G.E. AUSTIN AND M.M. REHFISCH Summary This chapter describes methods developed to predict the effects of climate change on the distribution and numbers of wintering waterbirds in Britain and Ireland. Models that predict the effect of habitat change resulting from sea-level rise (previously developed under MONARCH 1) were integrated with models that predict the redistribution of waterbirds with increasingly mild winters. This redistribution has been detected in the majority of waterbirds in which it has been investigated for the UK. The integrated modelling approach was applied and assessed for seven species (oystercatcher Haematopus ostralegus, ringed plover Chraradrius hiaticula, knot Calidris canutus, sanderling C. alba, dunlin C. alpina, curlew Numenius arquata and redshank Tringa tetanus) using the estuaries of the Suffolk coastline as a case study and subsequently applied within the Hampshire case study. This analysis has identified strong relationships between weather and waterbird winter distributions, and shown that already these species are redistributing in response to the changing climate. In particular, average minimum temperatures on the muddy estuaries of the east coast help explain a proportion of the variation in the distribution of six of the seven species considered. Although the distribution of four of the species considered is significantly associated with weather, in no species is this expected to result in a large increase in numbers on the estuaries of the Suffolk coast under the UKCIP02 scenarios. In most cases, consideration of the baseline predictions for the capacity of these estuaries suggests that there is currently surplus capacity and this situation is unlikely to change under the various predictions for sea level rise. Consequently, when considering those aspects of the birds' response to climate change that we have been able to model, there is probably little cause for concern that the estuaries of the Suffolk coast will not be able to hold the expected numbers of waders under the various UKCIP02 scenarios. There are still many other factors affecting waterbird distributions that remain unquantified and further work will be required before a qualitative tool upon which to base management targets for these natural resources is developed. The largest unknown amongst these is likely to be the effect of climate change on their Arctic breeding grounds and the availability of stop-over and wintering sites along their migration routes. While developing the models, initial work was done towards quantifying coastal climatic zones, used here to classify estuaries, based upon their winter weather. It is suggested that this aspect could be further developed to allow a bioclimatic modelling approach similar to that used within MONARCH for other taxonomic groups to be applied to coastal waterbirds. 5.1 Introduction In international terms, Britain and Ireland are ornithologically important, partly for the vast numbers of waterbirds that winter on its estuaries (Moser, 1987; Cayford and Waters, 1996; Rehfisch et al., 2003). The waterbirds are attracted by a combination of productive wetlands and relatively mild winters. Many estuaries have been designated SSSI or ASSI, SPA, or Ramsar (often all three) on the strength of the over-wintering waterbirds that they support. Monitoring waterbirds is relatively straightforward, and being near the top of the food chain the state of their populations provides a useful proxy for the "health" of the estuarine habitat more generally. Climate change has the potential to affect over-wintering waterbirds in two ways. Firstly, rising sea levels will directly affect the availability of the habitats and prey favoured by these birds, especially within an estuarine context, and, secondly; there will be the direct effect of changed meteorological conditions on the birds, their habitats and their food. Within MONARCH 1, as part of research into the impacts of climate change on coastal environments, both these aspects were explored. Within MONARCH 2 these relationships were further explored with the aim of developing modelling protocols that could be used to predict the


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impact of climate change at a local scale. Such models would make it possible to predict with a certain degree of confidence how many waterbirds would be present in an area according to the total numbers present in Britain and Ireland. As part of MONARCH 2, models were developed and then tested on the estuaries of the Suffolk coast and subsequently on those adjacent to the New Forest in the Hampshire case study area (see Chapter 6). 5.2 Methods 5.2.1 Modelling changes in waterbird distributions in relation to weather The research undertaken in MONARCH 2 considered the effects of changing winter weather on estuarine waterbirds. It has been established that the winter distribution of many wader species, as monitored by the Wetland Bird Survey (WeBS), has been changing over the past three decades, with a general pattern of shifts towards the north and east during this period (Austin et al., 2000; Austin et al., 2001; Rehfisch and Crick, 2003). Generalized Linear Models (GLMs) were used to investigate whether the numbers of estuarine waders counted at a site was related to local winter weather. This analysis showed that for four out of 10 wader species certain aspects of winter weather (mean wind speed or mean minimum temperature) significantly explained part of the variation in numbers. However, although statistically significant, the proportion of variation in bird numbers at a given site that was explained by the local weather was small and thus the predictive capabilities of these models were small. MONARCH 2 also reported on research undertaken by the BTO on behalf of the Wetland Bird Survey (WeBS) that had taken a broader view, in that it considered the proportion of the national population of each species wintering in one particular region and related this to the winter weather averaged around the British coast. This research offered a more promising method of predicting changes in distributions of waterbirds due to climate change. A GLM was formulated to specifically test the hypothesis that the proportion of birds wintering in Wales and southwest England, a region of Britain in which numbers of wintering waders had fallen steadily since the mid-1980s (Austin et al., 2000), was related to average coastal weather conditions elsewhere. The results suggested that increasingly mild winter weather in the east of England allowed an increasingly large proportion of the national population of eight of the nine common and widespread species considered to take advantage of the rich food supplies found on the relatively muddy east coast estuaries (Austin and Rehfisch, in press). It follows that as weather patterns continue to change with global warming, the distributions of wading birds are likely to continue to change in response. It was not known whether this approach would be transferable to other regions or scaleable to smaller regions or possibly individual sites. If transferable, it would make it possible to model how the distributions of wintering waders in Britain and Ireland might respond to climate change. As part of MONARCH 2 the WeBS modelling protocol was developed further to determine whether it would be transferable to other regions and scales within Britain and Ireland. 5.2.2 Modelling changes in waterbird densities in relation to sea level rise MONARCH 1 considered the effects that sea level rise can be expected to have on the extent and quality of estuarine habitats that support the majority of Britain and Ireland’s coastal waterbirds (Rehfisch et al., 2003). The BTO and Centre for Ecology and Hydrology (CEH), developed techniques to allow estuarine waterbird densities on British estuaries to be predicted from estuary sediments, morphology and geographical location. This technique was predisposed to making predictions of how waterbird densities could be affected by changes in the shape of estuaries with sea level rise. Accordingly, these methods were further developed as part of MONARCH 1 to produce a modelling protocol for predicting the densities of waterbirds that would be expected following changes in estuary morphology due to sea level rise and any managed response to that sea level rise (Austin et al., 2001, Austin et al., submitted). This was done for a range of waterbird species, and


those models for Haematopus ostralegus (oystercatcher), Calidris canutus (knot), C. alpina (dunlin), Numenius arquata (curlew) and Tringa totanus (redshank) proved robust to rigorous testing. These five species account for over 90% of the waders that winter on British estuaries (Rehfisch et al., 2003) and about 60% of those wintering on Irish estuaries (Colhoun, 2001). These models were applied to the two case study areas in MONARCH 2 (Austin et al., 2001; Austin and Rehfisch, 2003). For each estuary, expected changes in estuary morphology due to sea level rise were determined using detailed topographic data gathered using an airborne Light Detection and Ranging (LIDAR) remote sensing system and potential sea defence management plans, these data being provided by the Environment Agency. From these two case studies, a general pattern of change to estuarine habitat with sea level rise emerged. Where sea defences are maintained, the estuary morphology is expected to be relatively stable and the MONARCH models predicted no substantial changes in bird densities. Where a policy of managed realignment of sea defences is to be adopted, many estuaries can be expected to become wider as land is claimed or reclaimed by the sea, and in turn sediments would be expected to become increasingly sandy (Yates et al., 1995). The MONARCH models predict that in such cases the estuaries will support lower densities of species such as redshank, dunlin and curlew, which are found at greatest densities on muddier sediments (Austin et al., 1996). In contrast they would be capable of supporting higher densities of species such as oystercatcher that tend to favour sandier sediments. On estuaries where land claim for agriculture has been a historic feature, a substantial increase in estuarine area would be possible if the current sea defences were to be breached or if schemes for managed realignment were to be implemented, thus allowing agricultural land to revert to intertidal habitats. Where areas of land-claim have been urbanised or industrialised there is probably little scope for managed realignment. Where estuaries are bounded by hard natural features there is likely to be little substantial change in estuary shape and, particularly, area. The impact of sea-level rise will, therefore, differ between regions of Britain and Ireland depending on the regional differences in geology, isostatic realignment and historical land-claim. In England, the most vulnerable estuaries to sea-level rise are those in East Anglia and the southeast. These regions will experience relatively high mean sea-level rise due to the effects of isostatic adjustment and because the general topography has led to a history of land-claim (Austin et al., 2001), making the future of large areas of agricultural land in the region dependent on the maintenance and improvement of existing sea defences. Many of the estuaries in the area hold internationally important numbers of waterbirds (Musgrove et al., 2001). In contrast, in northern England, Wales and Scotland no estuaries have been identified as being susceptible to significant changes in shape due to rising sea levels (Frazier, 1999). In Ireland, estuaries with a history of significant land-claim are concentrated in the southeast. MONARCH, then, had established a preliminary modelling protocol for assessing the possible effects of sea level rise on estuarine waterbirds. As part of MONARCH 2 the aim was to integrate such predictions with those from the weather related models (section 5.2.1) to give an overall assessment of the consequences of global climate change on estuarine habitats and to test the modelling protocol on four estuaries in Hampshire. 5.2.3 Developing the regional waterbird and weather modelling protocol The WeBS modelling approach was developed to test the specific hypothesis that the downward trend in the numbers of many species of wader in Wales and southwest England was associated with increasingly mild winter weather. This association was found to be significant for a range of wader species. It was suggested that this could be due to growing numbers of their winter populations taking advantage of the increasingly mild winter weather, which made it energetically less expensive to winter on the relatively food-rich, muddy estuaries of the east coast, despite them remaining colder than the west (Austin and Rehfisch, in press). 5.2.3.1 The Wetland Bird Survey Weather Model Research undertaken by WeBS sought the existence of relationships between the proportion of the UK population of each species over-wintering on estuaries in the region of interest and the climatic


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conditions in the UK using a Generalised Linear Model. The proportion, i.e., the total population in the region of interest (Totalreg) divided by the total UK population (TotalUK) was modelled by logistic regression. The models were binomial and specified a logit link. Population values for each species were calculated as the sum of birds at all sites having first used the Underhill indexing algorithm (Underhill and Prŷs-Jones, 1994) to estimate missing counts. UK wader populations can vary between years and so differences in distribution could result from less attractive sites only supporting high numbers in years when UK populations are high. The WeBS models addressed this by including the UK national index (NATINDEX = TotalUK ÷ TotalUK for January on base

year) as an explanatory variable. The principal circumstance under which the UK national index might significantly improve a species model would be where movement into or out of the UK, either due to annual fluctuation or a long-term trend, has been disproportional between regions. The principal explanatory variables of interest for the WeBS research were MINTEMP, RAIN and WIND, the monthly averages of daily minimum temperature, rainfall and windspeed, respectively. Those values were derived from Meteorological data from the British Atmospheric Data Centre (BADC). Models were fitted to the data using a stepwise approach. Consequently the final model for each species would include those parameters from the equation: Logit(Totalreg÷TotalUK) = μ + β(MINTEMP) + γ(RAIN) + δ(WIND) + ε(NATINDEX) [Equation 1] that were significant in explaining the variation in the proportion of the UK population over-wintering in the target region. A positive parameter estimate for minimum temperature would indicate that during warmer winters a larger proportion of the species wintered in the target region. Similarly, positive parameter estimates for rain or wind would indicate that a higher proportion of the population was found in the target region during wetter or windier winters respectively. 5.2.3.2 Developments for MONARCH 2 Weather data The WeBS model had used weather variables derived by averaging Meteorological Office data for weather stations associated with estuaries. For MONARCH 2, it was desirable to make the baseline weather data as compatible as possible with the predicted scenarios. Thus, the UKCIP02 baseline data were used to derive weather variables for Britain. Similar data for Ireland were only available for Northern Ireland. Irish Meteorological Office data, therefore, were obtained to characterise weather in Ireland. The WeBS model had considered three weather variables (Equation 1), chosen because they could be expected to influence the energy budgets of estuarine waders. Although weather data from Ireland were to be incorporated into the MONARCH 2 analysis it was not apparent how raw Irish Meteorological Station data and interpolated UKCIP02 data could be combined into a single average variable for each weather parameter. This necessitated each weather parameter being averaged to give separate variables for, at least, Britain and Ireland. For compatibility across the whole of Ireland, Irish Meteorological Station data were combined with similar data for Northern Ireland previously obtained for MONARCH rather than use the UKCIP02 data for Northern Ireland. Extending the approach of calculating separate weather variables for each weather parameter for Britain and Ireland, the possibility of further subdividing weather variables to take account of the longitudinal clines in weather across Britain which lead to different conditions on the east and west coasts was explored. This would be advantageous as the WeBS analysis had suggested that it was east coast weather rather than coastal weather generally that was driving the observed shifts in wader distributions. Accordingly, a coastal climatic zonation of UKCIP02 data was derived for Britain (described in the following section).


Wader numbers on British estuaries vary markedly through the winter, normally reaching peaks during midwinter, although the exact phenology and patterns may differ between species and sites. The WeBS model had addressed this seasonal variation by considering month as an additional class variable to those given above in Equation 1. Within MONARCH 2, analyses that included data from Ireland were restricted to January because, firstly, bird data from other winter months were generally not available for many Irish sites and, secondly, the data obtained from the Irish Meteorological Office were restricted to January for budgetary reasons. Coastal Climatic Zones Coastal climatic zones were developed to determine appropriate geographical locations upon which to base a west - east split of the UKCIP02 baseline data for Britain for modelling purposes rather than to describe in detail a coastal zonation similar to the MONARCH bioclimatic zonation. The subset of UKCIP02 data derived from 5 km grid squares bisected by the British coastline were selected for each of the three weather parameters for December, January and February using a Geographic Information System (GIS, ESRI, 2002). These data were then subjected to a Ward’s minimum variance cluster analysis (SAS Inc., 2002) to produce classifications based on 3, 5, 10, 15 and 20 clusters. The results from these analyses were then plotted in the GIS. None of these classifications allowed the purely west / east split desired but comparing classifications allowed the geographical stability of boundaries between clusters to be assessed. Those boundaries that were geographically stable over a range of classification being those between coastlines with major climatic differences. With 10 clusters or more the general pattern was similar. In the case of the 10-cluster classification (Figure 5.1a) two clusters (2 and 5) accounted for all of the east coast of Britain between Beachy Head in the south and the Moray Firth in the North. Cluster 5 was also found concentrated in the upper reaches of a number of large west coast estuaries (The Severn, Mersey and Solway). The south coast of England between Beachy Head and Exmouth fell largely into cluster 4, which, together with clusters 1 and 3 accounted for most of the remaining coast of England and Wales. The remaining clusters were concentrated on the west coast of Scotland and the Western Isles, with clusters 6 and 7 tending to be associated with coastline exposed to westerly weather systems and clusters 8 to 10 tending to be associated with more sheltered coastline. The 15- and 20-cluster classifications tended to differ from the 10-cluster classification principally in that western Scotland and the Western Isles were further divided. The classification of the east coast of Britain was particularly stable between 20, 15 and 10 cluster classifications. The sought after division between east and west coast Britain became even more clear-cut with the 5-cluster classification (Figure 5.1b). The east coast of Britain between Beachy Head and the Moray Firth fell completely within cluster 3. The remainder of England, southern Scotland and the remainder of the north and east Scotland coasts fell largely into cluster 1. The final three clusters were principally confined to the west coast of Scotland and the Western Isles although cluster 2, which occurred on the more exposed coastline in Scotland, also occurred to a small degree in the shelter of estuaries in southwest England and Wales. Clusters 4 and 5 were associated with more sheltered coastline. The three cluster classification lost the sought after division between east and west coast Britain (Figure 5.1c). Clusters 1 and 3 from the 5-cluster classification merged to give a single cluster covering most of Britain other than the west of Scotland and the Western Isles. The persistence of zone boundaries associated with the east coast of Britain across all but the most restrictive classification suggested that weather variables representing the averages of the weather parameters for east coast Britain should be based on data from coastal grid cells between Moray Firth clockwise to Beachy Head. It also suggested that the remainder of Britain should be divided into two areas, broadly speaking the remainder of England and Wales and the west of Scotland and Western Isles. Weather variables for the south and west of Britain were, therefore, based on the averages of weather parameters for coastal grid cells from Beachy Head clockwise to Firth of Clyde. The west of


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Scotland and Western Isles do not contribute significantly to the bird data set being analysed here as there are no estuaries north of the Firth of Clyde supporting significant numbers of the principal "estuarine" wader species. Accordingly, weather variables for that region were not included in the models that follow because of the necessity of keeping the number of potential explanatory variables to a reasonable level. Figure 5.1a: Distribution of coastal weather zonation classes based on 10-cluster classification. Classes are numbered by decreasing frequency of occurence.

Cluster 1 of 10 Cluster 2 of 10 Cluster 3 of 10 Cluster 4 of 10 Cluster 5 of 10

Cluster 6 of 10 Cluster 7 of 10 Cluster 8 of 10 Cluster 9 of 10 Cluster 10 of

10 Figure 5.1b: Distribution of coastal weather zonation classes based on 5-cluster classification. Classes are numbered by decreasing frequency of occurence.

Cluster 1 of 5 Cluster 2 of 5 Cluster 3 of 5 Cluster 4 of 5 Cluster 5 of 5


Figure 5.1c: Distribution of coastal weather zonation classes based on 3-cluster classification. Classes are numbered by decreasing frequency of occurence.

Cluster 1 of 3 Cluster 2 of 3 Cluster 3 of 3

The MONARCH 2 models The new MONARCH 2 models were extensions of the WeBS model described above (Equation 1, section 5.2.3.1). The dependent variable was unchanged. The potential explanatory weather variables, which in the WeBS model had represented the UK average, were each replaced by up to three coastal zone averages, one each for the east coast Britain, the south and west coast Britain, and for Ireland. For these analyses, the national index, which in the WeBS model is referred to as the UK Index, was replaced by the British Index and, additionally, the Irish Index was considered for some models. For Britain, annual values for the weather variables were derived for each of the two retained coastal zones by averaging the January value for each of the three weather parameters, mean minimum temperature, mean rainfall and mean wind speed from those 5km grid cells in the UKCIP02 baseline data that are bisected by coastline. For Ireland, annual values for comparable weather variables were obtained by averaging the January value for each of the same three weather parameters from six Irish Meteorological Office and eight UK Meteorological Office weather stations from Northern Ireland, chosen for their proximity to the coast (Table 5.1). Table 5.1: Meteorological stations from which data were used to derive coastal weather parameters for Ireland.

Meteorological Station Location Balliwatticock 54° 32′ N 5° 40′ W Mourne Grange 54° 4′ N 6° 2′ W Jordontown 54° 41′ N 5° 53′ W Bann 55° 10′ N 6° 45′ W Lough Foyle 55° 4′ N 7° 4′ W Belmullet 54° 14′ N 10° 0′ W Casement Aerodrome 53° 18′ N 6° 26′ W Cork Airport 51° 51′ N 8° 29′ W Dublin Airport 53° 26′ N 6° 14′ W Malin Head 55° 22′ N 7° 20′ W Roche’s Point 51° 48′ N 8° 15′ W Rosslare 52° 15′ N 6° 20′ W Shannon Airport 52° 42′ N 8° 55′ W Valentia 51° 56′ N 10° 15′ W

For Britain, annual indices for each wader species were derived using standard WeBS methodology based on the Underhill indexing algorithm (Underhill and Prŷs-Jones, 1994). Thus indices were based


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on all sites where at least 50% of potential counts had been made, values for missing counts being imputed. In practice this includes all but a few estuaries holding very few birds. A similar approach was used to derive indices for Ireland, however, the coverage of estuarine sites was less complete. Thus Irish annual indices were based on data from eight sites in Northern Ireland that are covered by WeBS and, following consultation with the organisers of the Irish Wetland Bird Survey (I-WeBS), a representative sample of 12 estuarine sites from the remainder of Ireland (Table 5.2). The latter were chosen for the quality and quantity of their data and in order to obtain as wide a geographical coverage as possible. Table 5.2: Estuaries used to derive Irish wader indices.

Site Location Clonakilty Bay (Cork) Cork Harbour (Cork) Ballymacoda (Cork) Dungarvan Harbour (Waterford) Bannow Bay (Wexford) Inner Galway Bay (Galway) Baldoyle (Dublin) Broadmeadow (Malahide) Estuary (Dublin) Rogerstown Estuary (Dublin) Boyne Estuary (Louth) Dundalk Bay (Louth) Carlingford Lough Dandrum Bay Outer Airds Strangford Lough Belfast Lough Larne Lough Lough Swilly (Donegal) Lough Foyle Bann Estuary

51º 35' N 8º 52' W 51º 51' N 8º 17' W 51º 54' N 7º 55' W 52º 5' N 7º 37' W 52º 14' N 6º 48' W 53º 12' N 9º 1' W 53º 25' N 6º 8' W 53º 28' N 6º 10' W 53º 30' N 6º 0' W 53º 44' N 6º 15' W 53º 56' N 6º 19' W 54º 2' N 6º 10' W 54º 15' N 5º 49' W 54º 28' N 5º 32' W 54º 28' N 5º 37' W 54º 41' N 5º 50' W 54º 50' N 5º 47' W 54º 58' N 7º 39' W 55º 4' N 7º 4' W 55º 10' N 6º 45' W

While including separate variables in the analysis for different parts of Britain and Ireland might be expected to improve the resulting models, differences between the completeness of bird data and the quality of the weather data between Ireland and Britain caused a number of problems. When bird data from Ireland were excluded from the analysis all 30 years of bird data for Britain could be included. When Irish bird data were included in the models those models could only be based on nine winters (1984/85 to 1986/87 and 1993/94 to 1998/99), partly because no data were available for most other years for most of the Irish sites, and partly because the inclusion of those data for additional years from the remaining sites led to the overall level of imputed counts exceeding the accepted limits for the Underhill indexing algorithm. Although weather data were available for both Britain and Ireland for the entire period for which bird data were available, weather variables for Ireland were derived from a small number of weather stations. These data were thus more susceptible to being influenced by a few anomalous values and local conditions than were the equivalent UKCIP02 baseline (already smoothed to remove anomalies) based variables. To address these problems, three basic GLMs were considered for each species using, in each case, a stepwise approach for variable selection. Model BI1: Applicable to Britain and Ireland. These models considered as explanatory variables all three weather parameters for January only, for each of the east coast of Britain, the south and west coasts Britain and the Irish coast together with both British and Irish bird indices. Model GB1: Applicable to Britain only. These models considered as explanatory variables all three weather parameters, for both the east coast of Britain and the south and west coasts of Britain together


with the British bird index. These models are unaffected by any limitations of the Irish data. They do not allow for bird movements between Ireland and Britain or weather conditions in Ireland affecting the distribution of birds in Britain. As no Irish data were included, alternative versions based on January data only and based on December to February inclusive were possible. Model GB2: Applicable to Britain only. These models considered as explanatory variables all three weather parameters, for each of the east coast of Britain, the south and west coasts of Britain and the Irish coast together with the British bird index. These models, based on January data only, are unaffected by limitations of the Irish bird data but are only applicable to regions within Britain. Whereas they allow for weather conditions in Ireland affecting the distribution of birds within Britain, they do not allow for bird movements between Ireland and Britain. Determining the geographical extents over which the modelling protocol is reliable As stated earlier, it was not known whether this modelling protocol, although based on one that had successfully explained part of the variation in numbers of waders in southwest Britain, would be transferable to other regions or scaleable to smaller regions. Although numbers of waders have decreased in some areas and increased in others, numbers in some areas have changed little. In the latter cases it may be that weather conditions have not changed or that emigration has been balanced by immigration. Consequently underlying processes which may be driving waterbird redistributions, and which may include climate change, would not yet be evident in changing bird numbers (the dependent variable) and thus no significant associations would be expected. In order to determine the wider applicability of the modelling protocol, the models were run in an automated manner for a range of species, and for a range of incrementally sized regions (all single estuaries, all possible groupings of 3, 5, 7, 9, 11, 13 and 15 adjacent estuaries and estuaries grouped by EA or SEPA regions). These analyses were run using British sites only because bird data from groups of adjoining sites were not available for Ireland and thus the proportion of a population in a given region of Ireland (the dependent variable) could not be derived. Rather than develop the modelling protocol based on a single species as originally intended a range of species was used as it was felt that this would give a better assessment of the general applicability of the approach. Geographical areas where models might be expected to be obtained for each of seven species (Oystercatcher Haematopus ostralegus, Ringed Plover Chraradrius hiaticula, Knot Calidris canutus, Sanderling C. alba, Dunlin C. alpina, Curlew Numenius arquata and Redshank Tringa totanus) were assessed, within a GIS (ESRI, 2002), by plotting for each model and for each incremental increase in the number of adjacent sites being considered, the central site of each group of sites. Sites were displayed according to whether weather variables explained, significantly, part of the variation in the proportion of each species’ national population wintering on each group of sites. The results for all seven species are summarised below (Table 5.3) and maps for Dunlin are given by way of illustration (Figure 5.2). For each species models were obtained for up to 934 unique groups of adjacent sites for each of the three model forms (BI1, GB1 and GB2). With such a large number of models it was not feasible to critically assess each. Furthermore, particularly for the Britain and Ireland models (BI1), there was a high variable to sample size ratio. It was, therefore, necessary to assess the probability that models to be used for a particular study area could have been obtained by chance. This was done using randomisation techniques (Manly, 1991). Using 9999 repetitions, the dependent variable (over-winter average bird numbers) was matched randomly to the independent variables rather than matched to the relevant winter. The independent variables were not randomly sorted with respect to each other thus maintaining any relationships between the co-variates themselves. The proportion of cases where the model was deemed significant represents the probability of having obtained that model by chance. Consequently, it would be reasonable to accept a model with a particular set of explanatory variables if it was obtained less than 500 times from the 9999 repetitions (equivalent to P < 0.05).


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Table 5.3: Summarised results of geographical locations where models explaining bird distribution in terms of weather may be expected. In order to provide a geographical context, groups of adjacent sites were classified according to which of the three SEPA areas or six coastal EA regions the centre site lies within. Species are listed in taxonomic order (oc=oystercatcher, rp=ringed plover, kn=knot, ss=sanderling, dn=dunlin, cu=curlew, rk=redshank). Species codes are in uppercase when more than 50% of their regional models included weather (P<0.05) and lowercase when 25%-50% of their regional models included weather (P<0.05). Models were assessed at three different geographical scales: Local, one to five adjacent sites; Intermediate, seven to 11 adjacent sites, and; Extensive, 13 to 15 adjacent sites. Note that the true geographical extent of the three categories will differ between different areas of the country depending on the linear density of estuaries. Additionally models were considered for all estuaries contained within each of the SEPA or EA regions.

Local scale (1 to 5 adjacent sites) Model BI1 Model GB1 Model GB2SEPA - Highland, Grampian & Western Isles dn,cu dn,CU dn,cu SEPA – Southwest Area OC,dn,cu oc,cu oc,cu,rk SEPA – Southeast Area oc,kn,cu rp oc,rp EA – North East Region OC,rp,dn,cu,rk kn,dn oc,KN,dn EA – North West Region oc, kn, rk kn kn EA – Wales oc,rp,kn,ss,DN,rk kn,ss,DN DN EA – Anglian Region OC,rp,KN,dn,cu,rk dn dn,cu EA – South West Region oc,rp,kn,dn oc,rp,dn,cu oc,rp,dn,cu,rk EA – Southern Region kn,dn,rk dn,rk oc,rp,dn,RK Intermediate scale (7 to 11 adjacent sites) Model BI1 Model GB1 Model GB2SEPA - Highland, Grampian & Western Isles rp,DN,CU CU CU SEPA – Southwest Area oc,dn oc,rp,cu oc,cu SEPA – Southeast Area OC,dn oc,rp OC,rp EA – North East Region OC kn,cu oc,kn,cu EA – North West Region KN,ss,dn rp,KN,ss EA – Wales oc,rp,DN,rk ss,DN DN EA – Anglian Region OC,dn,cu cu oc,cu EA – South West Region oc,dn,cu,rk dn,CU CU EA – Southern Region oc,kn,dn,cu,RK rp,DN,rk oc,rp,DN,RK Extensive scale (13 to 15 adjacent sites) Model BI1 Model GB1 Model GB2SEPA - Highland, Grampian & Western Isles rp,DN,CU rp,CU,rk oc,CU SEPA – Southwest Area OC,RK cu,RK cu,RK SEPA – Southeast Area OC,dn,cu OC,rp OC,rp EA – North East Region OC,cu cu OC EA – North West Region oc,dn KN,dn,cu KN EA – Wales DN rp,dn dn EA – Anglian Region OC,dn CU oc,CU EA – South West Region oc,rk oc,rp,dn,CU oc,rp,dn,CU,rk EA – Southern Region oc,cu,RK dn,rk oc,dn,rk Regions (all sites within SEPA or EA boundary) Model BI1 Model GB1 Model GB2SEPA - Highland, Grampian & Western Isles RP,SS,DN CUjw oc,rp,kn,ss,dn,cu,rk SEPA – Southwest Area SSw oc,rp,kn,ss,dn,cu,rk SEPA – Southeast Area DN OCw,CUjw,RKjw oc,rp,kn,ss,dn,cu,rk EA – North East Region SS DNjw oc,rp,kn,ss,dn,cu,rk EA – North West Region RPw,KNjw,SSjw,CUjw oc,rp,kn,ss,dn,cu,rk EA – Wales OC,RP,DN RPw,KNw,SSjw,DNw,CUw oc,rp,kn,ss,dn,cu,rk EA – Anglian Region OC oc,rp,kn,ss,dn,cu,rk oc,rp,kn,ss,dn,cu,rk EA – South West Region OC,RP,KN oc,rp,kn,ss,dn,cu,rk oc,rp,kn,ss,dn,cu,rk EA – Southern Region oc,rp,kn,ss,dn,cu,rk oc,rp,kn,ss,dn,cu,rk Ireland as part of Britain &Ireland SS N/A N/A Northern Ireland as part of Ireland RP N/A N/A Republic of Ireland as part of Ireland RP N/A N/A


Figure 5.2: Assessment of the geographical areas where models were obtained in which weather explained, significantly, part of the variation in dunlin numbers between winters. Solid circle = central site of adjacent groups for which at least one weather variable was included in the model; shaded circles = central site of adjacent group of sites for which no models containing weather variable were obtained. These data are summarised for all species considered in Table 5.3. Number of adjacent sites in group

Dunlin Model BI1

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5.3 Suffolk coastline case study The Suffolk coastline was chosen for the first case study because its estuaries are surrounded by low-lying land previously claimed for agriculture and thus offering considerable scope for managed re-alignment in response to sea-level rise. Estuaries along this part of the coast have particularly muddy sediments which support high densities of invertebrates and thus of the wading birds that prey upon them. WeBS research suggests that increasingly mild winters in this part of Britain are driving many of the observed shifts in wader distribution as birds adjust their trade-off between the risk of cold weather mortality on this relatively cold coastline and the benefit of richer feeding conditions (Austin and Rehfisch, in press). 5.3.1 Impact of climate change Using the protocol detailed above (5.1), statistically valid models based on weather variables were obtained for four of the six species considered (Table 5.4). The models for oystercatcher and redshank predict the proportion of the British population of each to be found on the Suffolk estuaries excluding the Stour and Orwell. The Stour and Orwell estuaries were excluded from the analysis because bird numbers on these adjoining sites have been decreasing across a range of species in contrast to the remainder of the EA Anglian Region, probably due to local habitat degradation (Armitage et al., 2002). When these two sites were included no predictive models were obtained for any species. While no models for the Suffolk coast were obtained for either ringed plover or dunlin, models for these species were obtained for the whole of the EA Anglian Region. No model was derived for sanderling because in most winters none have been recorded by WeBS on these estuaries. Table 5.4: Details of the Generalised Linear Models relating the proportions of the British population of wader species over-wintering in EA Anglian Region or on the estuaries of the Suffolk coastline to weather. Applicability – the region was chosen to provide the most reliable models for each species. Parameters – independent variables retained by the model and the associated parameter estimates (β,γ,δ in equation 1). Partial t values and probability – indicates the significance of each parameter included in the model. Partial R2% – indicates the percentage of the variation in the dependent variable explained by each parameter. Adjusted model R2% – indicates the percentage of the variation in the dependent variable explained by the full model (adjusted for multiple parameters). Probability of obtaining the model by chance – as assessed by the randomisation testing using 9999 repetitions (see end of section 5.2.3.2).

Species Applicability Parameters

Parameter estimate

Partial t value Partial R2%

(adj. model R2%)

Probability of obtaining the model by

chance

Oystercatcher

Suffolk Coastline

Intercept East-coast Minimum Temperature

-7.775 0.277

t1=3.09, P=0.0044

24.8% (22.2%)

P=0.0303

Ringed Plover EA Anglian Region

Intercept East-coast Minimum Temperature West-coast Wind

0.132 0.179

-0.181

t1= 2.14, P=0.0414

t1= -2.42, P=0.0225

11.9% 14.9%

(21.6%)

P=0.0025

Dunlin EA Anglian Region

Intercept GB Index East-coast Minimum Temperature

-0.742 -0.229 0.147

t1= -4.30, P=0.0002 t1= 3.41, P=0.0020

27.3% 21.3%

(45.0%)

P=0.0015

Redshank Suffolk Coastline

Intercept East-coast Minimum Temperature

-3.853 0.119

t1= -2.62, P=0.0137

19.2%

(16.4%)

P=0.0254


The average minimum East coast temperature explained, significantly, part of the variation in the proportion of the British population wintering on the Suffolk or Anglian coast of all four species. In all cases the positive parameter estimate for this variable indicates that the higher the average minimum east coast temperature, the higher the proportions of the species over-wintering in Britain that do so on the estuaries of the Suffolk coast. This gives strong support for the hypothesis that temperatures on the east coast are driving the observed shifts in the distributions of waders wintering in Britain. However, the proportions of the variation explained by the weather variables in these models are comparatively low at between 12% and 25%. This indicates that factors other than the weather variables considered have an influence on wader distributions. Amongst such factors, water quality, human disturbance, predation pressure, and habitat loss are likely to be particularly important and are the subjects of ongoing research by BTO. Consequently, these weather based models, although being built upon highly significant associations, have relatively weak predictive power as can be seen from the wide confidence limits of the resulting predictions when the models are used to produce predictions for the proportions of these species over-wintering in EA Anglian Region or the estuaries of the Suffolk coast under the Low and High UKCIP02 scenarios (Figures 5.4a to 5.4d). For any given species the predictions are neither statistically different from the predictions made under the alternative scenarios nor the values recorded over the past three decades. Figure 5.4a: Observed numbers of oystercatcher over-wintering on the Suffolk estuaries between the winters of 1969/70 and 1999/2000 inclusive and predicted numbers (with 95% confidence intervals) made under the UKCIP02 Low and High scenarios for 2020s, 2050s and 2080s. The models used predicted the proportion of birds over-wintering in Britain that do so on the estuaries of the Suffolk Coast. These predictions have been converted to numbers using both minimum and maximum recorded values for birds over-wintering in Britain. Because the proportion is not dependent on country-wide numbers this gives an indication of the extremes in numbers that might be expected to over-winter on these estuaries.

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Oystercatcher - Numbers predicted under all UKCIP02 scenarios considered are well within the recorded range over the past three decades although below the average over that period (Figure 5.4a). For any given time-frame the differences between the predictions of numbers made under the High and Low scenarios are no larger than the observed between winter variation over the past three decades.


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Figure 5.4b: Observed numbers of ringed plover over-wintering on the Suffolk estuaries between the winters of 1969/70 and 1999/2000 inclusive and predicted numbers (with 95% confidence intervals) made under the UKCIP02 Low and High scenarios for 2020s, 2050s and 2080s. The models used predicted the proportion of birds over-wintering in Britain that do so on the estuaries of EA Anglian region. These predictions have been converted to numbers using both minimum and maximum recorded values for birds over-wintering in Britain. This gives an indication of the extremes in numbers that might be expected to over-winter on the estuaries within this region. The resulting predictions have been further adjusted to give those numbers expected on Suffolk estuaries while using both minimum and maximum values for the proportion of birds over-wintering in EA Anglian region that do so on these estuaries. This latter proportion is not dependent on country-wide numbers (otherwise a model based on the estuaries of the Suffolk coast alone would have been obtained). This gives a indication of the extremes in numbers that might be expected to over-winter on these estuaries.

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Figure 5.4c: Observed numbers of dunlin over-wintering on the Suffolk estuaries between the winters of 1969/70 and 1999/2000 inclusive and predicted numbers (with 95% confidence intervals) made under the UKCIP02 Low and High scenarios for 2020s, 2050s and 2080s. The models used predicted the proportion of birds over-wintering in Britain that do so on the estuaries of EA Anglian region. The Dunlin national index is an explanatory variable in the predictive model used. In order to obtain an indication of how the size of the population affects the estimates, separate predictions have been made while using minimum and maximum recorded values of the Dunlin national index from the past three decades. The resulting predictions have been further adjusted to give those numbers expected on the estuaries of the Suffolk coast while using the mean value for the proportion of birds over-wintering in EA Anglian the extremes in numbers that might be expected to over-winter on these estuaries.

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Ringed plover - Values predicted under all UKCIP02 scenarios considered are well within the recorded range over the past three decades and close to the average over that period (Figure 5.4b). For any given time-frame the difference between predictions made under the High and Low scenarios are small compared to the observed between winter variation over the past three decades. Dunlin - Numbers predicted under all UKCIP02 scenarios considered are generally within the recorded range over the past three decades (Figure 5.4c). However, the model for this species shows that in years when the number of birds over-wintering in Britain has been relatively low and during this period the proportion of birds wintering on the estuaries of the East Anglian coast has been relatively high. If this trend continues then the proportion of birds (although not the number) over-wintering there may increase beyond recent levels as dunlin forsake estuaries to the west of Britain and probably Ireland. Also, if the numbers of dunlin over-wintering in Britain and Ireland were to decline, although numbers on the estuaries of the East Anglian coast may remain relatively stable, the proportion of the British and Irish population supported by these estuaries would increase. Figure 5.4d: Observed numbers of redshank over-wintering on Suffolk estuaries between the winters of 1969/70 and 1999/2000 inclusive and predicted values (with 95% confidence intervals) made under the UKCIP02 Low and High scenarios for 2020s, 2050s and 2080s. The models used predicted the proportion of birds over-wintering in Britain that do so on the estuaries of the Suffolk Coast. These predictions have been converted to numbers using both minimum and maximum values for birds over-wintering in Britain. Because the proportion is not dependent on country-wide numbers this gives a indication of the extremes in numbers that might be expected to over-winter on these estuaries.

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Redshank - Numbers predicted under all UKCIP02 scenarios considered are well within the recorded range over the past three decades and close to the average over that period (Figure 5.4d). For any given time-frame the differences between the predictions of redshank numbers made under the High and Low scenarios are no larger than the observed between winter variation over the past three decades. 5.3.2 Impact of sea level rise Models have been developed for predicting the density of birds held on estuaries from aspects of estuary morphology for five species of wader – oystercatcher, knot, dunlin, curlew and redshank (section 5.2.2). The application of these models for predicting the change in capacity of estuaries to hold wader densities in response to estuary realignment has been demonstrated previously for the Deben estuary in Suffolk and the Duddon estuary in Cumbria (Austin et al. 2001).


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Following the protocol developed in MONARCH 1, management plans from the Suffolk Estuarine Strategies (EA, 1999) and predicted sea level rise under the UKCIP 2020s and 2050s High scenarios for climate change were incorporated into the ArcView Geographic Information System (ESRI, 2002). The necessary management plans were available for three Suffolk estuaries, the Deben, the Alde / Ore and the Blyth. Similar data were not available for the Stour and Orwell estuaries. The predictions of sea level rise used were those previously made for 2020s and 2050s under UKCIP Low and High scenarios (Austin et al., 2001). LIDAR data were used to assess whether management compartments defined in the management plans would be subject to inundation with sea level rise and thus predict the future shape of each estuary. Under the existing management strategies there is very little scope for significant changes to the shapes of the three estuaries considered (Figure 5.5). There would be a small increase in the overall area of both the Blyth and the Alde / Ore while the area of the Deben effectively remains at its current size. The changes to the values of the variables used by the MONARCH models were, therefore, small for all estuaries considered and consequently the nature of the sediments would not be expected to change sufficiently to have a substantial effect on the densities of birds that they are likely to support. This is because the basic shape of all the estuaries will remain "long and narrow" and thus sediments are likely to remain muddy. Accordingly, predictions for each estuary were only made under those management scenarios that would allow the greatest scope for realignment, i.e., allowing all management compartments, other than those designated as hold the line, to be included in the tidal regime where the LIDAR data indicated that they would be inundated by spring high tides with predicted sea level rise (Figures 5.6a-e). Furthermore, most of the area of those compartments that would no longer be defended under the management plan would be inundated under our predictions of sea level rise associated with the UKCIP Low scenario for the 2020s, thus leaving little scope for further change. Consequently the predictions under the various scenarios and time-frames would be expected to be similar. Oystercatcher – Relatively small numbers of oystercatcher winter on the estuaries of the Suffolk coast, a consequence of their preference for sandier sediments. The Blyth and Deben baseline predictions (model predictions under current estuary shape) are three-fold higher than, but not significantly different from, those observed over the past three decades (Figure 5.6a). This suggests that other factors not accounted for by the estuary morphology models such as the impacts of low shellfish stocks in the region (Atkinson et al., 2003) have held numbers of oystercatchers on these estuaries below those that they would otherwise have been capable of supporting. The baseline predictions for the Alde / Ore are within the range of values observed over the past three decades. As expected, there is little change in the predicted capacity of the estuaries to hold oystercatchers under the various scenarios for sea level rise. Proportionally small changes in capacity over their baseline values may be expected on the Blyth and Deben. The proportional increase in capacity of the Alde / Ore over its baseline value could be more substantial. Overall there could be an increased capacity for the estuaries of the Suffolk coast to support oystercatchers, however, the predictions under the various scenarios are not significantly different from those observed over the past three decades.


Figure 5.5: Suffolk Estuarine Strategies management compartments (left) and the maximum expected change (right) to the extent of the three estuaries considered under these management strategies and predicted sea level rise under the UKCIP 2020s and 2050s High scenarios for climate change.

Current extentSeawallCHaMPs dependentHold the lineManaged realignmentPossible defenceDo nothing

Predicted additional extent by 2050Predicted extent by 2020

Current extentSeawall

0 1 2 3 4 5 6 7 8 9 10 Km

Predicted ExtentManagement

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Figure 5.6a: Density and numbers of oystercatcher over-wintering on the Blyth, Alde / Ore and Deben. Observed values between the winters of 1969/70 and 1999/2000 inclusive and predicted estuary capacity to hold oystercatchers (with 95% confidence intervals) made under predictions of sea level rise for 2020s and 2050s.

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Knot – Historically numbers of knot on the estuaries of the Suffolk coast have been very low (Figure 5.6b). Knot tend to favour larger sites with wide intertidal areas such as The Wash and the Thames that are capable of supporting the large flocks formed by this species. Knot are also known to move between sites both within and between winters to a greater degree than most wader species (Rehfisch et al., 1996, Rehfisch et al., 2003). The patterns of occurrence on the estuaries of the Suffolk coast over the past three decades are typical of smaller sites with very few or no birds being recorded in most years, punctuated by years when substantial flocks are recorded. Because the models are based on average values the sporadic occurrence of knot on these sites leads to baseline predictions that are higher than in a typical winter but within the range of historic values. The predictions under the sea level rise scenarios do not differ substantially from those of the baseline and none of these differences


are statistically significant. Thus overall there is little to suggest that the capacity of the estuaries of the Suffolk coast to support Knot will change. Figure 5.6b: Density and numbers of knot over-wintering on the Blyth, Alde / Ore and Deben. Observed values between the winters of 1969/70 and 1999/2000 inclusive and predicted estuary capacity to hold knot (with 95% confidence intervals) made under predictions of sea level rise for 2020s and 2050s.

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Dunlin – Dunlin are found at their highest concentrations on muddy estuaries and consequently the estuaries of the Suffolk coast support relatively high densities of this species (Figure 5.6c). The model for dunlin is particularly good (explaining 84% of the variation in bird density - Austin et al., 2001) and the baseline predictions suggest that potentially these densities could be even higher. As expected, the predicted values under the various scenarios for sea level rise on any one estuary are all very similar. Those for the Blyth and Alde / Ore are higher than for the baseline while those for the Deben are lower - although these differences are not statistically significant. While overall there could be an


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increased capacity for the estuaries of the Suffolk coast to support dunlin the comparison between the baseline predictions and historic values suggest that there is already surplus capacity. Habitat availability in this area is therefore only likely to become limiting if the number of birds arriving on the Suffolk coast were to increase several-fold. Figure 5.6c: Density and numbers of dunlin over-wintering on the Blyth, Alde / Ore and Deben. Observed values between the winters of 1969/70 and 1999/2000 inclusive and predicted estuary capacity to hold dunlin (with 95% confidence intervals) made under predictions of sea level rise for 2020s and 2050s.

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Curlew – The number of curlew over-wintering on the estuaries of the Suffolk coast has increased steadily over the past three decades (Figure 5.6d). Although the predictive power of the model for this species is the least powerful being used (explaining 33% of the variation in bird density - Austin et al., 2001), comparison of the historic values with the baseline predictions suggest that while numbers


on the Deben may be close to capacity there is currently surplus capacity on the Blyth and Alde / Ore. While mindful of the wide confidence limits of the predictions for this species, those made under the various scenarios for sea level rise suggest that the capacity of the estuaries of the Suffolk coast to support curlew will remain sufficient to absorb a several-fold increase over current numbers. The only substantial change in curlew density is likely to occur on the Alde / Ore complex but this will be largely offset by the increase in area. Figure 5.6d: Density and numbers of curlew over-wintering on the Blyth, Alde / Ore and Deben. Observed values between the winters of 1969/70 and 1999/2000 inclusive and predicted estuary capacity to hold curlew (with 95% confidence intervals) made under predictions of sea level rise for 2020s and 2050s.

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Figure 5.6e: Density and numbers of redshank over-wintering on the Blyth, Alde / Ore and Deben. Observed values between the winters of 1969/70 and 1999/2000 inclusive and predicted estuary capacity to hold redshank (with 95% confidence intervals) made under predictions of sea level rise for 2020s and 2050s.

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Redshank – Redshank are found at their highest concentrations on muddy estuaries and consequently the estuaries of the Suffolk coast support relatively high densities of this species (Figure 5.6e). The model for redshank is particularly good (explaining 87% of the variation in bird density - Austin et al., 2001) and the baseline predictions suggest that potentially these densities could be even higher on the Deben and Alde / Ore complex. The model suggests that while densities on the Blyth and Deben are unlikely to change substantially with sea level rise, those on the Alde / Ore may decrease. However this decrease is not statistically significant and would be offset by the increase in area. Overall the capacity of the estuaries of the Suffolk coast to support redshank is unlikely to change much with sea level rise and is probably capable of absorbing a substantial increase in birds. Habitat


availability in this area is therefore only likely to become limiting if the number of birds arriving on the Suffolk coast were to increase several-fold. 5.3.3 Overall Impact The modelling has considered both how changes in climate might affect the distribution of waders within Britain and Ireland and the implications this may have for the numbers of these birds over-wintering on the estuaries of Suffolk coast; and how rising sea levels might affect the availability and nature of the intertidal habitat required by these birds. In order to assess the overall impact of climate change, predictions from these two processes must be considered together. Predictions have been made for both aspects in the case of oystercatcher, dunlin and redshank, climate models only for ringed plover, and sea level rise models only for knot and curlew. While other climate change driven factors will also impact on these birds it has not been possible to address these here. The largest unknown amongst these is likely to be the effect of climate change on their Arctic breeding grounds and the availability of stop-over and wintering sites along their migration routes (Lindström and Agrell, 1999; Rehfisch and Crick, 2003). It is possible that alternative sites nearer to the breeding grounds may become increasingly suitable for over-wintering. This may lead to the phenomenon of "short-stopping" whereby birds over-winter further to the north or east in areas previously unsuitable because of severe climate, and result in a reduction in the number of waders over-wintering in Britain and Ireland. Although the distribution of waders in Britain has changed (Austin and Rehfisch, 2003; Rehfisch et al., 2003) and over-wintering numbers of eight species have declined through the 1990s (Rehfisch et al., 2003) there is as yet no direct evidence for this effect in waders. Ongoing work being carried out under the WeBS partnership at a wider European scale may help determine whether the recent decline in wader numbers overwintering in Britain and Ireland can be explained by an increase in waders numbers over-wintering on continental Europe. It has already been recorded that some species of wildfowl are increasingly over-wintering in Eastern Europe on water-bodies that no longer remain frozen for long periods throughout the winter. The MONARCH 2 models provide predictions of how the number of waders over-wintering in Suffolk may change and whether with managed realignment there is likely to be sufficient habitat of suitable quality to support these numbers. Although the distribution of four of the species considered is significantly associated with weather (the proportion of waders in Britain that over-winter on the Suffolk or East Anglia coast being related to coastal minimum temperatures), in no case is this expected to result in a large increase in numbers on the estuaries of the Suffolk coast under the UKCIP02 scenarios if the present relationships remain true. In most cases, consideration of the baseline predictions for the capacity of these estuaries suggests that there is already surplus capacity and this situation is unlikely to change under the various predictions for sea level rise (Table 5.5). Consequently, when considering those aspects of the birds' response to climate change that we have been able to model, there is probably little cause for concern that the estuaries of the Suffolk coast will not be able to hold the expected numbers of waders under the various UKCIP02 scenarios. 5.4 Discussion and conclusions The coastal work tackled under MONARCH 2 has been successful in providing further support for the hypothesis that changes in weather patterns over the past three decades have resulted in a broad scale redistribution of waders over-wintering in Britain. In particular, average minimum temperatures on the muddy estuaries of the east coast help explain a proportion of the variation in the distribution of six of the seven species considered. Winter temperatures are thus likely to be at least one of the causal factors leading to the redistribution of over-wintering waders in Britain. This is an important result because relatively few studies (Parmesan and Yohe, 2003; Root et al., 2003) have been able to demonstrate a link between changes in faunal or floral distributions and changes in climatic conditions and yet such a mechanism is an inescapable assumption of all models that seek to predict future changes in distributions based upon contemporary associations including MONARCH.


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Table 5.5: Comparison of the numbers of wader predicted for the estuaries of the Suffolk coast against predicted capacity. The range given for baseline capacity are the values obtained by summing respectively the lower and upper 95% confidence limits, for the number of each species as predicted by the sea level rise models, based on current estuary morphology, across all the Suffolk estuaries. The range given for baseline numbers is that obtained from recorded values, summed across those estuaries for the winters 1969/20 to 1999/2000. The range given for capacity under UKCIP scenarios are the values obtained by summing respectively the lower and upper 95% confidence limits based on future predictions of estuary morphology across the Suffolk estuaries. The range given for numbers under the UKCIP scenarios are the values obtained by summing, respectively, the lower 95% confidence limit of numbers estimated under minimum recorded GB index and upper 95% confidence limit of numbers estimated under maximum recorded GB index, across these estuaries.

Prediction Oystercatcher Ringed plover Knot Dunlin Curlew Redshank

Capacity 0 - 4744 N/A 0 - 4264 16396 - 42438 848 - 5229 6165 - 12007 Baseline Numbers 11 - 605 24 - 102 0 - 438 2475 - 5184 307 - 771 537 - 3485

Capacity 0 - 6369 N/A 0 - 5210 19461 - 50983 385 - 4945 6570 - 12218 2020 Low Numbers 23 - 900 18 - 109 N/A 2242 - 5149 227 - 1036 494 - 4304

Capacity 0 - 7463 N/A 0 - 6046 21590 - 57287 348 - 5402 7199 - 13527 2020 High Numbers 24 - 931 18 - 110 N/A 2273 - 5211 226 - 1036 501 - 4363

Capacity 0 - 6691 N/A 0 - 5444 20266 - 53188 400 - 5161 6860 - 12756 2050 Low Numbers 26 - 1035 19 - 114 N/A 2371 - 5417 226 - 1033 521 - 4558

Capacity 0 - 7776 N/A 0 - 6269 22288 - 59281 357 - 5527 7341 - 13907 2050 High Numbers 31 - 1256 21 - 122 N/A 2539 - 5819 224 - 1029 556 - 4937

The coastal work has been less successful in its second aim of developing these associations into universal tools that could be used for conservation management at the local scale. However, the approach may work in some regions. The "estuary morphology models" provide a robust and reliable method for predicting densities of a variety of wader species from estuary morphology (Austin et al., 1996; Austin and Rehfisch, 2003) and thus the capacity of estuaries to hold these birds. However, it is only appropriate to apply these models to estuaries where the managed response to sea level rise allows a fairly substantial re-alignment of the estuary extent and consequentially a change to the values of the morphological variables used by those models. This condition was met only for the Alde / Ore estuaries on the Suffolk coast and not for the estuaries of the New Forest coast (see Chapter 6). Although based on highly significant associations, the proportion of variation in wader distributions explained by winter weather alone is relatively low because of the many other factors such as land-claim, water quality and recreational disturbance that also influence the distributions. However, from those aspects of the birds' response to climate change that we have been able to model, the Suffolk estuaries should have the capacity to hold numbers of waders similar to those at present under the various UKCIP scenarios. Thus the overall approach taken has successfully captured those aspects of change in wader distributions that can be expected in response to changes in estuary morphology with sea level rise and to continued direct response to changes in winter weather. However, a substantial part of the variation in wader distributions still remains unquantified. This overall approach provides a useful qualitative tool for assessing the direction in which these factors may drive future wader distributions, but, given that many other factors unrelated to climate change appear to have influenced past distributions, the approach has proved to be less useful as a quantitative tool upon which to base targets for the management of these natural resources. However, the importance of developing models for predicting waterbird distributions cannot be over-stated, as waterbirds are the principal feature of most coastal SPAs designated under the Habitats Directive (Stroud et al., 2001). Consequently, further research towards this goal is warranted.


The modelling approach adopted for other species within the MONARCH project is adaptable to coastal waterbirds. This would require the development of coastal winter bioclimatic zones and we have gone some way towards creating them. The models would need to be adapted to work with densities rather than simple presence / absence data and to incorporate annual weather data rather than a baseline average given that distributions have been changing over the past three decades (Austin and Rehfisch, in press; Rehfisch et al., 2003). Such an approach may prove more successful at integrating changes due to sea level rise with changes due to weather patterns. However, work towards incorporating the effect of other factors such as land-claim, water quality and disturbance into the models is also recommended. This is because whatever the modelling approach adopted, the likelihood of predicted distributions of any species, waders or otherwise, being realised will be related to the amount of observed variation that can be explained by the factors considered by the models and to the availability of realistic and accurate future scenarios of these factors. A cautionary note is needed, resulting from the approach that has been taken for predicting the likely effect of climate change on wintering waterbirds. The available waterbird data come from a survey that has collected data at relatively fine spatial and temporal scales for longer and in a more complete manner than that available for the large majority of surveys of other faunal or floral groups in the world (Musgrove et al., 2001). The observed changes in the British wader distributions have been clearly associated with changes in winter weather (Rehfisch et al., 2003; Austin and Rehfisch, in press). The habitat associations of waders are better known than for most other biological groups (e.g., Goss-Custard, 1977a; Goss-Custard, 1977b; Quammen, 1982; Goss-Custard and Yates, 1992; Yates et al., 1993; Goss-Custard et al., 1994; Goss-Custard 1995; Yates and Goss-Custard 1997; Rehfisch et al., 1999; Rehfisch et al., 2000). Many other aspects of wader biology have been extensively studied. However, even with this wealth of data and interpretative information, and ignoring the likely considerable impacts of climate change on the Arctic breeding grounds of waders (Lindström and Agrell, 1999; Rehfisch and Crick, 2003), it is still proving difficult to predict clear effects of climate change on the wader distributions due to the amount of unexplained variation in the sound models used to describe wader habitat usage and distributional change with weather. It is important for all predictive work on faunal distributions, whether based on this approach or bioclimatic relationships, to at least attempt to determine the size of the likely error in the predictions; otherwise the value of the predictions may be questioned. Even if these errors prove to be large, the MONARCH predictive assessment should still provide scenarios of likely change that can help guide national conservation priorities towards wildlife, and to help assess the approach that should be taken towards the anthropogenic factors that are leading to the climatic change. 5.5 References Atkinson, P.W., Clark, N.A., Bell, M.C., Dare, P.J., Clark, J.A. and Ireland, P.L. (2003). Changes in commercially fished shellfish stocks and shorebird populations in the Wash, England. Biological Conservation, 114, 127-141. Armitage, M.J.S., Burton, N.H.K., Atkinson, P.W., Austin, G.E., Clark, N.A., Mellan, H.J. and Rehfisch, M.M. (2002). Reviewing the Impact of Agency Permissions and Activities on Bird Populations in Special Protection Areas: Level 1 Interpretation. BTO Research Report No. 296 for the Environment Agency. British Trust for Ornithology, Thetford, Norfolk, UK. Austin, G. and Rehfisch, M.M. (in press). Shifting non-breeding distributions of migratory fauna in relation to climatic change. Global Change Biology Austin, G. and Rehfisch, M.M. (2003). The likely impact of sea level rise on waders (Charadrii) wintering on estuaries. Journal for Nature Conservation, 11, 43-58. Austin, G.E., Rehfisch, M.M., Viles, H.A., and Berry, P.M. (2001). Impacts on coastal environments. In: Harrison, P.A., Berry, P.M. and Dawson, T.P. (Eds.) Climate Change and Nature Conservation in


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Britain and Ireland – modelling natural resource responses to climate change (the MONARCH project). UKCIP Technical Report, Oxford, pp177-228. Austin, G.E., Peachel, I. and Rehfisch, M.M. (2000). Regional trends in coastal wintering waders in Britain. Bird Study, 47, 352-371. Austin, G.E., Rehfisch. M.M., Holloway, S.J., Clark, N.A., Balmer, D.E., Yates M.G., Clarke, R.T., Swetnam, R.D., Eastwood, J.A., le V. dit Durell, S.E.A., West, J.R. and Goss-Custard, J.D. (1996). Estuaries, Sediments and Shorebirds III: Predicting Wader Densities on Intertidal Areas. BTO Research Report No. 160 to ETSU (T/04/00207/REP). British Trust for Ornithology, Thetford, Norfolk. Boyd, H. and Madsen, J. (1997). Impacts of global change on arctic-breeding bird populations and migration. In: Oechel, W.C., Callaghan, T.Gilmanov, T., Holten, J.I., Maxwell, B., Molau, U. and Sveinbjornsson, B. (Eds). Global change and Arctic terrestrial ecosystems. Springer-Verlag, New York, USA, pp. 201-217. Cayford, J. and Waters, R. (1996). Population estimates for waders (Charadrii) wintering in Great Britain, 1987/88-1991/92. Biological Conservation, 77, 1-17. Colhoun, K. (2001). Irish Wetland Bird Survey 1998-1999: Results of the fifth season of the Irish Wetland Bird Survey; Including summarised results from Northern Ireland. Bird Watch Ireland. Environment Agency (1999). Suffolk Estuarine Strategies Phase II – Report C Deben Estuary. Environment Agency, Anglian Region, Peterborough. ESRI (2001). Environmental Systems Research Institute, Inc. USA. Frazier, S. (Ed.) (1999). A Directory of Wetlands of International Importance. Wetlands International and Ramsar Convention Bureau. CD. Goss-Custard, J. (1977a). The ecology of the Wash 3. Density related behaviour and the possible effects of the loss of feeding grounds on wading birds (Charadrii). Journal of Applied Ecology, 14, 721-739. Goss-Custard, J.D. (1977b). Predator responses and prey mortality in redshank, Tringa totanus (L) and a preferred prey, Corophium volutator. Journal of Animal Ecology, 46, 21-35. Goss-Custard, J.D. and Yates, M.G. (1992). Towards predicting the effect of salt-marsh reclamation on feeding bird numbers on the Wash. Journal of Applied Ecology, 29, 330-340. Goss-Custard, J.D., Caldow, R.W.G., Clarke, R.T., Durell, S.E.A le V.dit, Urfi, J. and West, A.D. (1994). Consequences of habitat loss and change to populations of wintering migratory birds: predicting the local and global effects from studies of individuals. Conservation: the Science and the Action (eds. Coulson, J and Crockford, N.J.). Ibis, 137, S56-66 Goss-Custard, J.D. (1995). Effects of habitat loss and habitat change on estuarine shorebird populations. Coastal Zone Topics: Process, Ecology & Management 1: 61-67 Lindström, Å. and Agrell, J. (1999). Global change and possible effects on the migration and reproduction of arctic-breeding waders. Ecological Bulletins. 47, 145-159. Manly, B.F.J. (1991). Randomization and Monte Carlo methods in biology. Chapman and Hall, London, UK.


Moser, M.E. (1987). A revision of population estimates for waders (Charadrii) wintering on the coastline of Britain. Biological Conservation, 39, 153-164. Musgrove, A.J., Pollitt, M.S., Hall, C., Hearn, R.D., Holloway, S.J., Marshall, P.E., Robinson, J.A. and Cranswick, P.A. (2001). The Wetland Bird Survey 1999-2000: Wildfowl and Wader Counts. BTO/WWT/RSPB/JNCC, Slimbridge. Parmesan, C. and Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37-42. Quammen, M.L. (1982). Influence of subtle substrate differences on feeding by shorebirds on intertidal mudflats. Marine Biology, 71, 339-343. Rehfisch, M.M., Austin, G.E., Freeman, S.N., Armitage, M.J.S. and Burton, N.H.K. (2004). The possible impact of climate change on the future distributions and numbers of waders on Britain's non-estuarine coast. In: Rehfisch, M.M., Feare, C., Jones, N.V. and Spray, C. (eds), Coastal Birds and Climate Change. Ibis, 146 (suppl 1), 70-81 Rehfisch, M.M. and Crick, H.Q.P. (2003). Predicting the impact of climate change on Arctic-breeding waders. Wader Study Group Bulletin, 100, 86-95. Rehfisch, M.M., Austin, G.E., Armitage, M.J.S., Atkinson, P.W., Holloway, S.J., Musgrove, A.J. and Pollitt, M.S. (2003). Numbers of wintering Waterbirds in Great Britain and the Isle of Man (1994/1995-1998/1999): II. Coastal waders (Charadrii). Biol. Conservation, 112, 329-341. Rehfisch, M.M., Austin, G.E., Clark, N.A., Clarke R.T., Holloway, S.J., Yates, M.G., Durrel, S.E.A. le V. dit, Eastwood, J.A., Goss-Custard, J.D., Swetnam, R.D. and West, J.R. (2000). Predicting densities of wintering Redshank Tringa totanus from estuary characteristics: a method for assessing the likely impact of habitat change. Acta Ornithologica, 35, 25-32. Rehfisch, M.M., Holloway, S.I., Yates, M.G., Clarke, R.T., Austin, G., Clark, N.A., Durell, S.E.A., le V. dit, Eastwood, J.A., Goss-Custard, J.D., Swetnam,R.D. and West, J.R. (1999). Predicting the effect of habitat change on waterfowl communities: a novel empirical approach. In: Goss-Custard, J. Rufino R.& Luis A. (eds.) Predicting Habitat Loss, pp. 116-126. HMSO, London. Rehfisch, M.M., Austin, G.E., Freeman, S.N., Armitage, M.J.S., and Burton, N.H.K. (2004). The possible impact of climate change on the future distributions and numbers of waders on Britain’s non-estuarine coast. In: Rehfisch, M.M., Feare, C., Jones, N.V. and Spray, C. (eds), Coastal Birds and Climate Change. Ibis. Root, T.L., Price, J.T. Hall, K.R. Schneider, S.H., Rosenzweig, C. and Pounds, J.A. (2003). Fingerprints of global warming on wild animals and plants. Nature 421, 57-60. SAS (2001). The SAS Institute Inc. Cary, NC, USA. Stroud, D.A., Chambers, D., Cook, S., Buxton, N., Fraser, B., Clement, P., Lewis, P., McLean, I., Baker, H. and Whitehead, S. (2001). The UK SPA network: its scope and content. Volume 1: Rationale for the selection of sites. JNCC, Peterborough, UK. Underhill, L.G. and Prŷs-Jones, R. (1994). Index numbers for waterbird populations. I. Review and methodology. Journal of Applied Ecology, 31, 463-480. Yates, M.G., Clarke, R.T., Swetnam, R.D., Eastwood, J.A., Durell, S.E.A. le V. dit, West, J.R., Goss-Custard, J.D., Clark, N.A., Holloway, S.J. and Rehfisch, M.M. (1996). Estuary, Sediments and


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Shorebirds I. Determinants of the Intertidal Sediments of Estuaries. A report by the Institute of Terrestrial Ecology under contract to ETSU (ETSU Project T/04/00201/REP). Yates, M.G., Goss-Custard, J.D., McGrorty, S., Lakhani, K.H., Durell, S., E.A. le V. dit, Clarke, R.T., Rispin, W.E., Moy, I., Yates, T., Plant, R.A., Frost, A.E. (1993). Sediment characteristics, invertebrate densities and shorebird densities on the inner banks of the Wash. Journal of Applied Ecology, 30, 599-614. Yates, M.G. and Goss-Custard, J.D. (1997). The development of a correlative approach relating bird distribution and remotely-sensed sediment distribution to predict the consequences to shorebirds of habitat change and loss. In: J.D. Goss-Custard, R. Rufino & A Luis (Eds.) Predicting and detecting the effect of habitat loss and change on wetland bird populations, ITE Symposium no. 30; Wetlands International publication no. 42. HMSO, London, pp. 138-144.

3 Species distribution and dispersal modelling

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