rangeMapper: a platform for the study of macroecology of...

MACROECOLOGICALMETHODS

rangeMapper: a platform for the studyof macroecology of life-history traitsgeb_739 945..951

Mihai Valcu1*, James Dale1,2 and Bart Kempenaers1

1Department Behavioural Ecology and

Evolutionary Genetics, Max Planck Institute

for Ornithology, Seewiesen, Germany,2Institute of Natural Sciences, Massey

University, Albany Campus, Auckland, New

Zealand

ABSTRACT

Aim To introduce rangeMapper, an R package for the study of the macroecologicalpatterns of life-history traits, and demonstrate its capabilities using three casestudies. The first case study addresses an important topic in conservation biology:biodiversity hotspots. Specifically, we investigate the congruence between globalhotspots of three parameters that describe avian diversity: species richness,endemic species richness and relative body mass diversity. The second case studyinvestigates a topic of relevance for macroecology: the inter-specific relationshipbetween range size and body size for avian assemblages, and how it varies geo-graphically. The third case study tackles a methodological problem in macroecol-ogy: the influence of map resolution on statistical inference, i.e. the question ofwhether and how the relationship between species richness and body mass varieswith map resolution.

Innovation rangeMapper offers a tight integration of spatial and statistical toolsfor macroecological projects and it relies on a high-performance database enginewhich makes it suitable for managing projects using a large number of species.rangeMapper’s architecture follows closely the concepts described by Gaston et al.(2008 Journal of Biogeography, 35, 483–500) and its flexibility allows for bothcomplex data manipulation procedures and easy implementation of new functions.By choosing case studies to cover various technical and conceptual issues we dem-onstrate rangeMapper’s capabilities to address a wide array of questions.

Main conclusion rangeMapper (http://cran.r-project.org/package=rangeMapper) is an open source front end software which can be used to addressquestions in both fundamental ecological research and conservation biology.

KeywordsBirds, body mass, breeding range size, functional diversity, hotspots, R package,species distribution, species richness.

*Correspondence: Mihai Valcu, DepartmentBehavioural Ecology and Evolutionary Genetics,Max Planck Institute for Ornithology, EberhardGwinner Strasse, 82319 Seewiesen, Germany.E-mail: [email protected] of this article is permitted in accordancewith the Terms and Conditions set out athttp://wileyonlinelibrary.com/onlineopen#OnlineOpen_Terms

INTRODUCTION

Understanding patterns of biological diversity is a major goal of

macroecological research. In particular, over the last decade sub-

stantial effort has been invested in explaining geographical

variation in species richness (e.g. Gaston, 2000; Davies et al.,

2007). In addition to species diversity, functional trait diversity

has now been recognized as an equally important component of

biological diversity (e.g. Petchey & Gaston, 2006). Indeed, with

the increased availability of life-history data compiled for whole

taxonomic groups, the first studies on life-history traits at a

global level have recently been undertaken (e.g. Jetz et al., 2008;

Olson et al., 2009).

Global patterns of species richness and life-history trait dis-

tributions are likely driven by multiple factors, at multiple levels

of organization (from population to assemblage level) and at

multiple spatial scales (from regional to global) (Gaston et al.,

2008). This makes macroecology inter-disciplinary in both its

approach and the tools it uses (Blackburn, 2004; Smith et al.,

2008). For example, a typical macroecology project (e.g. Hurl-

bert & Jetz, 2007; Olson et al., 2009; Fritz & Purvis, 2010;

Powney et al., 2010) requires a tight integration of geospatial

species range vector data with (1) life-history information and

(2) satellite remote-sensing ecological and climatological raster

data. The conceptual and technical complexity of such a project

is increased further depending on whether the level of analysis is

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Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2012) 21, 945–951

© 2012 Blackwell Publishing Ltd DOI: 10.1111/j.1466-8238.2011.00739.xhttp://wileyonlinelibrary.com/journal/geb 945

at the inter-specific or the assemblage level. For an inter-specific

analysis, the parameters required are the life-history traits of

interest for each species and the phylogenetic relationships

between species, linked with spatial characteristics of the species

ranges (e.g. range size or range shape) and the key environmen-

tal variables within the ranges. In contrast, for an assemblage-

level analysis, the geographical ranges of all species are

partitioned into a regular grid, and each ‘assemblage’ is defined

as the community of species that occurs within each cell. Each

assemblage can then be described by its richness (i.e. the count

of all species in a grid cell) and the life-history characteristics of

its species community. Environmental variables at each grid cell

are then typically used as predictor variables for both species

richness and/or life-history traits.

The analyses described above require adequate statistical

models that allow for spatial autocorrelation and/or phyloge-

netic control. Although various statistical tools already exist

along with spatial and database management support, these are

often difficult to integrate under the same computing platform.

Thus researchers typically have to switch between various com-

puter programs that are loosely interconnected at best and often

function as a black box (i.e. when not open source).

Here we introduce rangeMapper (http://cran.r-project.org/

package=rangeMapper), a versatile framework for the study of

macroecological patterns of life-history traits that can be used to

answer a large array of questions in both fundamental ecological

research and conservation biology. rangeMapper is an open

source extension for R (R Development Core Team, 2010), built

using R’s comprehensive database (James, 2010) and spatial

classes support (Pebesma & Bivand, 2005; Bivand et al., 2008;

Hijmans & Etten, 2010). Macroecological projects can be per-

formed at both inter-specific and assemblage levels, and tools for

connecting the two approaches are provided. rangeMapper

further allows a straightforward integration of the many statis-

tical tools existing in R.

In this paper we first describe the concept behind rangeMap-

per and introduce its general capabilities. Second, we apply

rangeMapper to three case studies chosen to cover various tech-

nical and conceptual issues. For each case study, we provide a

brief introduction to the topic, a description of the method, the

rangeMapper results and a brief discussion. These examples are

based on a comprehensive dataset of the geographical breeding

distributions of more than 8000 avian species. The case studies

are accompanied by reproducible examples using the life-history

traits and the breeding range distribution of the New World

wrens (Troglodytidae), a dataset which is bundled with the

package (see Appendices S1–S5 in the Supporting Information).

MATERIALS AND METHODS

The rangeMapper general framework

rangeMapper adopts a modular framework (Fig. 1) where each

project is partitioned into several steps. This versatility ensures

that it is relatively straightforward at each step to plug in various

statistical models of any degree of complexity. This mechanism

allows us to implement both (1) range structure indexes (e.g.

range shape; Pigot et al., 2010) and (2) measures of environmen-

tal parameters (e.g. mean primary production or elevation

range). Environmental parameters can be computed either

within the range of each taxon or at each grid cell (with the

support from the raster and rgdal packages; Hijmans & Etten,

2010; Keitt et al., 2010). Finally, the modular framework allows

the use of a wide array of statistical models computed at each

Figure 1 rangeMapper’s workflow. A regular grid (canvas) of a given resolution and coordinate reference system (CRS) is set (a),geographical ranges are overlaid on the canvas (b) and parameters are optionally computed for chosen range traits (c). Once thegeographical ranges are interpolated on the canvas, life-history traits (d) and environmental data (e) can be imported. Maps are saved tothe project (f) and can be retrieved in a format derived from class Spatial (Pebesma & Bivand, 2005). Maps can be customized forvisualization in R (Neuwirth, 2007; Bivand, 2009) (g) and exported for visualization (e.g. in Quantum GIS Development Team, 2011) or forspatial analysis (e.g. Rangel et al., 2010) (f). All modules are tightly interconnected and checks are performed at every step to ensure dataintegrity. The ‘SUBSET’ option of the mapping module (f) allows for the creation of new maps based on data at both species level (rangetraits, life-history traits) and assemblage level (existing maps, environmental data). An across-platform graphical user interface is availablefor modules (a) to (g), yet rangeMapper can also be used for scripting and simulations (h) (see Appendices S2–S4). function(x, y. . .) indicateswhich modules allow integration of existing or user defined functions (e.g. glm-s, range shape indices).

M. Valcu et al.

Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd946

canvas cell, from simple analyses (count, average) to rather

complex models applied at each pixel (e.g. generalized linear

models).

A ‘subsetting’ mechanism used for map building is an addi-

tional strength of rangeMapper. That is, subsets of life-history

traits, range traits, assemblage traits (i.e. pixel traits) or any

combination of those can be easily defined, ensuring a tight

connection between inter-specific and assemblage levels.

rangeMapper projects are hosted on disk in sqlite databases so

most computations are not memory limited. Moreover the

robust sqlite engine allows efficient management of large

projects. For example creating a global map of median body

mass of 8434 species using a canvas with a grain of 50 km2 takes

2.1 min on a four-core 2.8 GHz Intel Xeon running 64-bit

Ubuntu Linux 10 with 11.8 GB of physical memory.

For users without knowledge of R scripting language, a cross-

platform graphical user interface (GUI) is provided for most

tools (Valcu & Dale, 2011). Finally, rangeMapper is built using S4

classes (Chambers, 2008) and can therefore be easily extended.

Case-study datasets

We collected body mass data of 8434 bird species from the CRC

handbook of avian body masses (Dunning, 2008). When multiple

entries per species were available we used median body mass as

the species value. We digitized breeding ranges (i.e. the geo-

graphical extent of occurrence of each species in the reproduc-

tive season) from various sources (Cramp & Simmons, 1977–

1994; Brown et al., 1982–2004; Marchant & Higgins, 1990–2006;

del Hoyo et al., 1992–2010; Ridgely & Tudor, 2009) onto a 720 ¥360 unprojected pixel template of the earth and converted these

raster images into vector files. The hotspot congruence analysis

and the ‘range size–body mass’ analysis were performed using a

rangeMapper project with an equal area projection canvas

approximating to a 1° scale.

Case study 1: congruence of different biodiversity hotspots

Biodiversity ‘hotspots’ are a central concept in conservation

biology (Orme et al., 2005; Ceballos & Ehrlich, 2006) because

they form the foundation for establishing global conservation

priorities. However, because there is an ongoing debate about

which specific biodiversity measure is most relevant, it is impor-

tant to understand the extent to which different types of

hotspots overlap.

We identified the global hotspots of three parameters describ-

ing avian diversity: total species richness, endemic species rich-

ness and relative body mass diversity. This was done by

generating maps for species richness (total number of species in

each canvas cell), endemic species richness (number of species

with the 25% smallest breeding ranges present in each canvas

cell, e.g. Orme et al., 2005) and relative body mass diversity

(coefficient of variation of log10 body mass in each cell) (Fritz &

Purvis, 2010). The maps of endemic species richness and body

mass relative diversity can be found in Appendix S1. We defined

hotspots for each of these measures as the richest 5% of grid

cells (Fig. 2). We then measured the congruence between

hotspots by the extent of spatial overlap, i.e. the percentage of

canvas cells which met the definition of two or three of the

hotspots (Orme et al., 2005; Ceballos & Ehrlich, 2006).

We found a very low spatial overlap (0.9%) between hotspots

of species richness and relative body mass diversity (Fig. 2). The

overlap between hotspots of endemic species and relative body

mass diversity was also low (2.3%, Fig. 2). The overlap between

hotspots of endemic species and species richness was 3% and the

cumulative overlap between the three sets of hotspots was only

0.15%.

Our results suggest that the three avian diversity hotspots

considered here are virtually independent. This replicates previ-

ous results showing little congruence between hotspots of

species richness and endemism (Orme et al., 2005; Ceballos &

Ehrlich, 2006). Adding relative body mass diversity as a comple-

mentary biodiversity measure did not change the overall

picture, suggesting that spatial patterns exhibited by various

aspects of biodiversity are determined by different mechanisms.

This case study illustrates the use of rangeMapper for identi-

fying biodiversity hotspots (see Appendix S2 for a reproducible

R code example using the wrens dataset). Using rangeMapper’s

flexible subsetting mechanism, this example can be further

refined. For example, by changing the subset definitions to

incorporate only certain groups of species, we could investigate

hotspot congruency for particular taxonomic clades, different

functional groups or different habitat classes.

Case study 2: geographical variation in the relationship between

range size and body size

The relationship between species range size and average body

size is a classic topic in macroecology (e.g. Gaston & Blackburn,

1996, 2000). The relationship is positive across many taxa, but a

few studies report a negative relationship or no relationship.

Gaston & Blackburn (1996) suggested that negative relation-

ships are artefacts because the likelihood of finding a negative

(or no) correlation between range size and body size is higher

when the scale (i.e. the extent) of the study is too small to

encompass all the geographical ranges of the studied species.

Alternatively however, global-scale spatial variation in the range

size–body size relationship itself may exist. This is not far-

fetched, because spatial variation in similar relationships has

been documented. For example, the generally positive correla-

tion between range size and local abundance does not apply for

an entire biogeographical area, even though it is one of the most

robust findings in macroecology (Symonds & Johnson, 2006).

Here, we investigated global spatial variation of the slope of

the correlation between range size and body mass for avian

assemblages. For each grid cell, we estimated the slope of the

range size–body mass regression using a robust regression (Ven-

ables & Ripley, 2002) whereby both range size and body mass

were log-transformed and standardized with z-scores (i.e. scaled

and centred). This alternative to ordinary least squares regres-

sion ensures an unbiased estimation even when the model

assumptions are unfulfilled.

Macroecology of life history traits

Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd 947

Our analysis revealed strong geographical variation in the

slope of the range size–body mass regression despite the fact that

98.9% of the slopes were positive (Fig. 3a). Relatively strong

range size–body mass regression slopes were confined to certain

geographical areas. Moreover, after controlling for multiple

testing only 52.2% of the grid cells contained a statistically sig-

nificant range size–body mass regression (Fig. 3b). Interestingly,

among the four avian studies reviewed by Gaston & Blackburn

(1996) the two studies that reported negative or no relationships

(3 and 4 in Fig. 3b) examined areas where the positive relation-

ship was non-significant or negative, while the two studies

reporting positive relationships (1 and 2 in Fig. 3b) were from

areas where the positive relationship was strong and statistically

significant.

This case study illustrates the use of rangeMapper for statisti-

cal modelling at grid cell level (see Appendix S3 for a reproduc-

ible example using the wrens dataset). Although here we used a

robust regression slope, other statistical models can be incorpo-

rated equally easily in rangeMapper. For example, one could use

the slope of a mixed model (Pinheiro & Bates, 2000) (see Appen-

dix S3) that included higher taxonomic groups as random

effects and would thus allow for a certain degree of phylogenetic

correction.

Case study 3: the influence of grid size on the relationship

between species richness and body size

Spatial patterns of species richness can be strongly dependent on

the chosen resolution (i.e. grid cell size). When the spatial reso-

lution is not adequate, species richness obtained from geo-

graphical ranges is a poor estimator of true species richness

(Hurlbert & Jetz, 2007). Moreover, the effect size of predictors of

species richness can change with varying spatial resolution

(Rahbek & Graves, 2001; Davies et al., 2007). Therefore it is

a

b

cFigure 2 Global distribution of threemeasures of biodiversity hotspots definedas the richest 5% of grid cells (orange).(a) Hotspots of body mass diversity(coefficient of variation of log bodymass). (b) Hotspots of species richness.(c) Hotspots of endemic species. Overlapbetween hotspots of body mass diversitywith hotspots of (b) species richness and(c) endemic species, respectively, areindicated in blue.

M. Valcu et al.


reasonable to predict that the relationships between species rich-

ness and life-history variables will also depend on the spatial

resolution. For example, Olson et al. (2009) showed that species

richness was a strong predictor of variation in median body size

in an assemblage-level spatial multiple regression using a 1°

resolution (see Fig. 4a,b).

Here we examined the relation between median body mass

and species richness at the assemblage level using 100 different

spatial resolutions. We used rangeMapper projects set on equal

area projection canvases ranging from 50 to 550 km2 (i.e. from c.

0.45 to c. 4.5°) with median body mass (log10-transformed) as

the dependent variable and species richness (square-root trans-

formed) as the only predictor. To account for spatial autocorre-

lation we used simultaneous autoregressive models (SAR)

(Bivand et al., 2008).

The strength of the body size–species richness relationship,

quantified as the slope of a SAR model, varied considerably at

the 100 different spatial resolutions. We confirmed that the SAR

slope is negative for the entire resolution range, but found it to

decrease dramatically with increasing grid cell size (i.e. decreas-

ing map resolution; Fig. 4c). Typically such analysis is done for

three to four (Davies et al., 2007) or sometimes 10 resolutions

(Rahbek & Graves, 2001). However, because rangeMapper easily

allowed us to use a large number of spatial resolutions we could

additionally resolve a nonlinear dependence of the SAR slope on

the spatial resolution (Fig. 4d).

This case study exemplifies a more advanced use of

rangeMapper because it requires the command line interface or

batch processing, while the first two case studies can be per-

formed entirely from the GUI. However, the compact scripting

language makes it possible to implement complex analyses with

relatively few commands. In Appendix S4 we show how to itera-

tively build up projects of increasing resolution and extract the

parameters of interest (i.e. the slope of the body size–species

richness regression) associated with each level of resolution.

The techniques presented in this case study can be combined

with the ones described for case study 1 to investigate another

important methodological problem in macroecology – the

influence of range size on statistical inference (Jetz & Rahbek,

2002; Tello & Stevens, 2010). Appendix S5 shows the influence of

range size on the body mass–species richness regression slope

using the case study on the wrens.

CONCLUSION

rangeMapper is an open source front end R package for macro-

ecological studies designed to serve as an interface between the

spatial and the statistical tools offered through the R environ-

ment. By choosing three case studies covering various technical

and conceptual issues and a dataset of the global geographical

distribution of more than 8000 bird species we demonstrated

rangeMapper’s capabilities to address a wide array of questions.

a

b −1.0

−0.5

0.0

0.5

1.0

1

34

2

Figure 3 (a) Global distribution of the slope of the standardized range size–body size regression. (b) Statistically significant (P < 0.05) cellsafter controlling for multiple testing (Benjamini & Yekutieli, 2001) are shown in red. Numbers on the map refer to avian studies reviewed inGaston & Blackburn (1996, Table 1): (1) North American land birds, (2) Australian birds, (3) British birds, (4) Finnish passerines.



FINAL REMARK

When you use rangeMapper, we ask you to cite R together with

this publication followed by the package version you used.

ACKNOWLEDGEMENTS

We thank Allen Hurlbert and three anonymous referees for

helpful comments on an earlier version of this manuscript.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online

version of this article:

Appendix S1 Maps of endemic species richness and body mass

relative diversity.

Appendices S2–S5 R code accompanying case studies.

As a service to our authors and readers, this journal provides

supporting information supplied by the authors. Such materials

are peer-reviewed and may be re-organized for online delivery,

but are not copy-edited or typeset. Technical support issues

arising from supporting information (other than missing files)

should be addressed to the authors.

BIOSKETCHES

Mihai Valcu is a behavioural ecologist with a strong

interest in spatial ecology. His research focuses on

spatial aspects of avian reproductive behaviour and

life-history strategies.

James Dale is a senior lecturer in ecology at Massey

University. His research mostly focuses on social

behaviour in animals, with an emphasis on

communication, sexual selection, individual recognition

and reproductive strategies.

Bart Kempenaers is a behavioural ecologist interested

in the diversity of life-history traits. He is director of

the Department of Behavioural Ecology and

Evolutionary Genetics, Max Planck Institute for

Ornithology.

Editor: José Alexandre F. Diniz-Filho



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