Climate change vulnerability assessment of forest plants in the Credit River watershed: An application of NatureServe's CCVI tool
By Madaleine Sansom
A capstone submitted in conformity with the requirements for the degree of Master of Forest
Conservation
Daniels Forestry
University of Toronto
January, 2020
2
Executive Summary
Climate change is the most important conservation challenge of our lifetime. Conservation organizations and researchers can no longer assume stable climate averages and need to account for climate vulnerabilities when planning management and conservation activities. Climate vulnerability assessments allow for fine-scale information on species natural history and functional traits to be considered. The three pillars of this type of assessment are 1) exposure to climate change, 2) sensitivity to these changes, and 3) the adaptive capacity of a species or system. There are several methods for assigning climate vulnerability, among which the NatureServe Climate Change Vulnerability Index (CCVI) is the most widely used. In the present study, I used the NatureServe CCVI to assess 30 forest plant species in the Credit River watershed including forbs, ferns, shrubs, and trees. Credit Valley Conservation (CVC) is responsible for protecting and managing approximately 1000 km2 of land in southern Ontario. The land within CVC’s jurisdiction is largely fragmented and encompasses several municipalities and major cities. As climate change becomes a real issue, CVC needs a way to understand its effects on the natural heritage that exists within its watershed.
My objectives were to: 1) conduct a climate change vulnerability assessment of forest plants within the Credit river watershed, using the CCVI tool; 2) identify the key factors contributing to species’ vulnerabilities; 3) use existing bioclimatic envelope models for several tree species within the Credit River watershed and rankings from other CCVI projects in nearby areas, to support or dispute species ranks; and 4) weigh the benefits and limitations of the CCVI tool, and provide recommendations on how it could be used by organizations like CVC in the future.
Future climate conditions under Representative Concentration Pathway (RCP) 4.5 indicate overall drying from 38-58 mm as indicated by the Hamon (AET:PET) moisture metric, and a 3°C increase in mean annual temperature by 2050. Upon ranking each species, 13% are “less vulnerable”, 43% are “moderately vulnerable”, 37% are “highly vulnerable” and 7% are “extremely vulnerable”. The factors that contributed most to vulnerability were historical and physiological hydrological niche, history of pathogens or natural enemies, dispersal and movement capabilities, history of genetic bottlenecks, and genetic variation, consecutively.Theses results align with previous CCVI assessments in nearby geographic regions including the Ontario Great Lakes basin, Michigan, and West Virginia. Additionally, the rankings generally agree with bioclimatic envelope modelling for tree species in the Credit River watershed under climate change.
Moving forward, I recommend that CVC: 1) Conduct more detailed assessments using the CCVI, and work with other organizations on larger-scale assessments; 2) Develop a plan for assisted migration of species with more southerly seed zones, and Carolinian species; 3) Conduct a study to determine whether phenological mismatch is affecting spring ephemerals; and 4) Develop public education campaigns centred around climate change impacts on natural heritage.
3
Acknowledgments
I would like to thank Dr. Laura Timms (CVC) for bringing this project to my attention and
for her support and confidence through its duration. Thanks to CVC for providing me with the
opportunity to pursue my work as an intern. I would also like to thank Dr. Sandy Smith for
providing invaluable advice and continually challenging me as I worked through the many
phases of the project. Lastly, I would like to thank Dr. Danijela Puric-Mladenovic for her
involvement in the later stages of this project, and for lending her expertise in climate change
modelling and working within the complex framework of conservation in southern Ontario.
4
Table of Contents Executive Summary ....................................................................................................................................... 2
Acknowledgments ......................................................................................................................................... 3
Introduction .................................................................................................................................................. 6
Literature review .......................................................................................................................................... 7
NatureServe CCVI ...................................................................................................................................... 9
Making use of multiple methods ............................................................................................................ 10
Objectives ................................................................................................................................................... 12
Methodology .............................................................................................................................................. 12
Study area ............................................................................................................................................... 12
Species selection ..................................................................................................................................... 14
Species occurrence maps ........................................................................................................................ 15
Climate data ............................................................................................................................................ 15
Species life history information .............................................................................................................. 16
Mapping vulnerability of dominant tree species in ELC communities ................................................... 16
Results......................................................................................................................................................... 16
Historic and future climate exposure ..................................................................................................... 16
CCVI rankings .......................................................................................................................................... 17
Factors most affecting vulnerability ....................................................................................................... 19
CCVI rankings and other assessment areas ............................................................................................ 19
Mapping vulnerability by ELC type ......................................................................................................... 21
Discussion ................................................................................................................................................... 21
Climate results and uncertainty .............................................................................................................. 21
Factors affecting vulnerability/ rankings ................................................................................................ 22
Comparing results to previous work ....................................................................................................... 25
Mapping vulnerable communities .......................................................................................................... 26
Benefits and limitations .......................................................................................................................... 27
Recommendations ..................................................................................................................................... 28
Future vulnerability assessments ........................................................................................................... 28
Other work .............................................................................................................................................. 28
Conclusion .................................................................................................................................................. 28
References .................................................................................................................................................. 30
5
Appendix A: Figures ................................................................................................................................... 36
Appendix B: Tables ..................................................................................................................................... 38
Appendix C: List of references used to obtain CCVI rankings ................................................................... 42
6
Introduction
The Credit Valley Conservation (CVC) Authority manages and protects the Credit River watershed in southern Ontario, an area of approximately 1000 km2 (CVC, 2009). The watershed encompasses portions of the regions of Peel, Halton, Wellington, and Dufferin and contains many different vegetation communities. The most significant physiographic features in the Credit River Watershed are the Niagara Escarpment, the Oak Ridges Moraine, and the Lake Ontario Shoreline. Because of its location, and position within Ontario’s busiest cities, including Mississauga, the watershed is largely fragmented in its Southern regions which poses many threats to the health of the riparian ecosystems and surrounding forests.
The three zones of the watershed include this most southern portion, or lower watershed, extending as far north as Norval (Fig. 1; CVC, 2009). The central watershed extends from Inglewood to Georgetown and encompasses part of the Oak Ridges Moraine and Niagara Escarpment. The landscape in this area is especially diverse and includes many wetlands and forests and has historically been used primarily for agriculture, with a few smaller cities and conservation areas. The upper watershed extends as far as Orangeville and is largely dominated by mixed coniferous-deciduous forests and farms. As the level of development and infrastructure increases from North to South water quality of the river and streams in the watershed also declines (CVC, 2009).
The challenges that face the Credit River watershed are not far different than those faced by other urban-rural watersheds in
southern Ontario. The existing Natural areas are generally restricted to the middle and upper watershed, and closely follow the Credit River (Fig. 1). As a result, the watershed management differs across the CVC due to distribution of natural heritage and land use types, as well as due
Figure 1: Land-use in the three section of the Credit River watershed (source: Chapter 3, Credit River Watershed Report, 2012. Retrieved from https://cvc.ca/watershed-science/watershed-monitoring/credit-river-watershed-health-report/chapter
7
to different priorities that municipalities in the watershed have. Cumulative effects of habitat fragmentation, pollution, and climate change put the pressure on the remaining natural areas. Despite long-term monitoring, restoration, and local engagement, that all contribute to a better understanding of the watershed and surrounding ecosystems and help improve existing environmental (CVC, 2009), watershed management needs to account for climate change adaptation. Conservation authorities like CVC need tools to incorporate climate change threats into both short and long terms management.
CVC works in close partnership, and receives funding from various municipalities including Peel Region, Halton Region, and the Cities of Mississauga, Brampton, and Caledon. As the neighbouring watershed to the TRCA’s jurisdiction, CVC also collaborates with the TRCA on larger scales initiatives. Some of the most recent examples of this type of work can be seen in the Climate Trends and Future Projections in Peel Region report (Auld et al., 2016) and the Natural Systems Vulnerability to Climate Change in Peel Region (Tu et al., 2017). This work was carried out by Peel Region, with the help of representatives from the TRCA, the Ontario Climate Consortium (OCC), and CVC. These two reports have set the stage to understand the impacts of climate change at a regional scale and are useful in that Peel Region covers approximately half of CVCs jurisdiction.
Literature review Natural resources management and conservation groups are faced with one of the most
important challenges climate change. Its effects are not isolated to specific regions and species and it can amplify and worsen the effects of human activities already putting stress on natural systems (Akbari et al., 2001; Greene & Millward, 2016). Climate was once considered to be a constant and its impact predictable on most systems, however it has now become one of the greatest uncertainties (Stein et al., 2014). Adaptation and mitigation are two ways in which society can respond to climate change (Glick et al., 2011). Current conservation policy and planning has turned focus towards adaptation since that the threats of climate change are better understood (Glick et al., 2011; Pearson & Dawson, 2003). This requires managers to have an in-depth understanding of what species, habitats or ecosystems are most susceptible to the effects (Füssel & Klein, 2006; Stein et al., 2014), and thus highlights the need to develop tools to assess where intervention may be needed.
The study of climate change has commonly focused on the impact assessments as they enable
evaluate what effects different climate scenarios are likely to have on natural systems (Füssel &
Klein, 2006). The ability to narrow down effects due to specific CO2 concentrations and levels of
warming may have on water resources, crops, and other life-supporting systems allows for
identification of threats. However, this approach does not deal with adaptation to those threats
(Feenstra et al., 1998; Moriondo et al., 2011). Impact assessments are useful in raising
awareness about the degree of climate change and the potential long-term effects (Pearson &
Dawson, 2003), and helping to inform and creating new policies. That is not to say that impact
assessments should not be used, alone however they do not provide sufficient information on
how to respond to changing climate and should be used in conjunction with other methods
including vulnerability assessments (Füssel & Klein, 2006; Jones, 2001).
8
Species vulnerability assessment (VA) is an effective way to identify how and why certain species, based on their life history and niche requirements, may be more vulnerable than others to climate change (Young & Hammerson, 2016). The common metrics for VAs include evaluating exposure to changes in climatic factors (i.e., mean annual temperature, seasonal precipitation, and growing season length), the sensitivity of the species/ system to changes, and its adaptive capacity (Glick et al., 2011). These three characteristics act as pillars for vulnerability assessments and can be applied across spatial scales and different ecosystem types (Stein et al., 2014; Young & Hammerson, 2016). Though vulnerability assessments provide important insight in the future and what climate change could bring, it should not be used alone, but rather in combination with other methods and tools informing conservation prioritization for climate change adaptation (Stein et al., 2014). There are different VA methods that range from species distribution modelling, to frameworks and various tools that give species ranks.
Species distribution models (SDMs) are generally the most commonly used modelling
approach in VAs (Sinclair et al., 2010; Willis et al., 2015). These models usually assume that thee
existing species’ distribution is at equilibrium with the environment and do not include the
species potential for adaptation or dispersal limitations (Austin et al., 2011). SDMs also tend to
assume that correlation between species’ range changes and changing climatic factors is a
causative relationship, which is another flaw (Sinclair et al., 2010). The method also requires an
in-depth understanding of modelling approach and can be inaccessible to many people (Austin
et al., 2011). Additionally, predictive models can be developed based on different approaches
which makes comparing results across different applications challenging (Sinclair et al., 2010).
Trait-based assessments (TVAs) are also frequently-used for VA and involve the use of tools or frameworks to rank species or habitat vulnerability based on a set of traits (Glick et al., 2011). Generally, TVAs require less technical and modeling knowledge, making their use more accessible. inputs include information on species’ functional traits and habitat requirements. Their output is a score or ranking that indicates how vulnerable a given species is to the effects of climate change. TVAs assessment can be conducted based on four tools developed: the United States Environmental Protection Agency (EPA) released the “Framework for categorizing the relative vulnerability of threatened and endangered species to climate change” in 2009, a document which outlines four modules used to determine overall vulnerability and the confidence of a given score (EPA, 2009). The United States Forest Service System for Assessing the Vulnerability for Species to climate change (SAVS) was released in 2011 (Bagne et al., 2011). The Climate Change Sensitivity Database (CCSD) was released in 2012 in collaboration between the University of Washington and the Nature Conservancy. The NatureServe Climate Change Vulnerability Index (CCVI) tool was released for the first time in 2011, with the most recent Canadian-specific version released in 2015 (Appendix B, Table 1 provides details on each VA method).The SAVS and CCSD are both online tools that are openly accessible whereas the CCVI is formatted in a MS Excel workbook that can be downloaded for free as well.
9
NatureServe CCVI The CCVI is one of the most widely-used TVA tools as it can be applied to a range of
taxonomic groups, from aquatic to terrestrial, and from plants to animal species (Pacifici et al., 2015; Stein et al., 2014; Young & Hammerson, 2016). The CCVI’s widespread use has been also attributed to its ability to rapidly assess a large number of species and produce a confidence score for each ranking (Pacifici et al., 2015). The CCVI was designed to help managers screen large numbers of species and identify which ones are the most vulnerable and what factors contribute most to that vulnerability (Young et al., 2016). As such, it has become a popular tool used by government, non-profit, and academics (Young et al., 2015). A recent survey revealed that the CCVI is now the most common method being used by state agencies to assess the impacts of climate change on wildlife as part of federally-mandated revisions to their state wildlife action plans (AFWA, 2012).
A review conducted in 2015 ( Young et al., 2015 ) surveyed previous users and identified challenges and different approaches to using the tool (Young et al., 2015). The most common reason for using CCVI was to “generate a broad understanding about climate change”, and “to address species management decisions” (Young et al., 2015). All studies were targeted at managers or scientists directly involved in species management decisions. In most cases, the CCVI was the only method considered, however in three cases the CCVI was used in conjunction with other methods (distribution models/ scenario-based spatial analysis). The main strengths identified were that the CCVI was easy to use, had detailed guidelines, the ability to screen multiple species from variety of taxa, a consistent and repeatable framework, and ability to compare confidence in scores among species (Young et al., 2015).
Specific USA and Canadian CCVI versions have been created in response to feedback from users (Young et al., 2015) and have been released in 2016. The tool is regularly updated, and the newest release of the Canadian version has several improvements. This included a means to “assess climate effects in overwintering areas of migratory species when they are not present within the assessment area” (Young et al., 2015), two more interspecific factors (sensitivity to pathogens/natural enemies and sensitivity to competition from native/non-native species), and a new factor that considers plant reproductive systems (Young et al., 2015). The tool developers have been quick to make improvements based on feedback from the users. As the tool has grown in popularity, also more studies citing the CCVI are being published, often in the form of reports for specific areas or groups of species. Several studies have used Nature Serve tool for breeding birds in Alaska (Liebezeit et al., 2012), rare plant species in California (Anacker et al., 2013), the Lake Simcoe watershed (Brinker & Jones, 2012), and the entire Great Lakes basin (Brinker et al., 2018).
Brinker et al. (2018) and Ontario Ministry of Natural Resources and Forestry (OMNRF) conducted a TVA using the Canadian release of the CCVI for species in the Ontario Great Lakes basin. They selected species based on their susceptibility to climate change, whether they were habitat specialists or keystone species, species of conservation concern (provincially or globally rare), those which inhabit the southern or northern limits of their suitable range, and those which have sufficient data available to conduct an assessment (Brinker et al., 2018). One of the main limitations that Brinker et al., 2018 noted is that the tool does not allow for the
10
assessment of conservation threats unrelated to climate change. They suggested that it be used in conjunction with other status assessments, such as NatureServe Conservation Status ranking (Brinker et al., 2018).
To cope with the uncertainty of the effects of climate change, and the discrepancies in scoring at different spatial scales, Griffis-Style et al. (2018) took a slightly different approach than most studies using the CCVI to assess the vulnerability of desert herpetofauna. They used spatial at data from the “conterminous United States and the southwestern desert regions” (Griffis-Kyle et al., 2017) to calculate exposure. Climate data from the Coupled Model Intercomparison Project (CMIP) for four different emissions scenarios were used to determine future climates. Each species was ranked using both sets of spatial data and each climate change scenario, then scores were averaged to represent the overall response of the species (Griffis-Style et al., 2018). While this novel approaches to VA can be taken by CCVI tool-users to suit their own needs it causes some discrepancies when attempting to compare scores from a wide range of studies.
Making use of multiple methods Both SDMs and TVAs have their limitations and uncertainties, however these can be
reduced, or cross-validated when both methods are employed in one assessment. Willis et al.
(2015) outline the need to compare SDMs and TVAs and used a case study of two African bird
species to show the different results that they produce. The results of this showed that SDMs
do not consider a species’ ability to migrate and barriers that might impede migration under
climate change. Even if the results show that species will have good habitat in the future, if they
cannot access that habitat due to dispersal limitations, then they may be more vulnerable than
the model indicates. As TVAs are so accessible and easy-to-use, SDMs are often not done or
overlooked, which is a critical flaw in vulnerability assessments since simple is not always the
best solution (Willis et al., 2015).
Information from SDMs can be incorporated into TVAs, as was done by Anacker et al.
(2013) by including species’ modelled response to climate change in the CCVI tool assessment.
This is an optional field within the tool which is useful, should information be available. They
used both the Nature Serve CCVI to determine general categories of vulnerability, and then
Maxent (Phillips, 2017) to run SDMs to determine if and how each species range might be
reduced under climate change and if the new range overlaps with the historical range (Anacker
et al., 2013). The CCVI score excluding the SDM resulted in different species in the top five most
vulnerable species list. They indicate that, due to the variability in modelled results based on
model type and parameters used, quantitative modelling alone does not provide enough
consistency to predict the fate of rare species under climate change. Overall, they conclude that
using multiple methods can provide a broader picture of climate change vulnerability but that
index-based methods are an excellent place to start (Anacker et al., 2013).
Similarly, Still et al. (2015) used CCVI and Maxent to conduct a vulnerability assessment of
rare plants in the western United States. Unlike Anacker et al. (2013), they did not include
11
modelled response in the CCVI score to compare SDM and index results without overlap. Some
species not scored as vulnerable under the CCVI proved to be highly vulnerable using the SDM
method. Overall, their results indicate that the CCVI and SDM can be used to compliment each
other and to further prioritize species for management. The two approaches can cross-validate
results and decrease uncertainty leading to greater confidence in results and more informed
decision-making (Still et al., 2015).
The need to conduct binational assessments that transcend political boundaries has been acknowledged as a key issue in VAs. This is of particular concern when assessing climate change vulnerability because if a species’ range is expected to shift, this usually means it will shift into different jurisdictional boundaries. A 2017 study (Rempel & Hornseth, 2017) evaluated the effectiveness of using the CCVI with SDMs to assess the vulnerability of migratory birds in the US and Canadian Great Lakes Basin. Three focal species were selected and overall, the CCVI allowed for a basic assessment of vulnerability. Gaps in data and monitoring across jurisdictions did, however, prove to be a challenge and required the authors to extrapolate some information (Rempel & Hornseth, 2017).
The NatureServe CCVI is quickly becoming the most widely-used TVA tool as it has used by
government departments. For example, it has been mandated for use in the revision of state
wildlife action plans in the US (AFWA, 2012). It has also been used by the OMNRF for two
studies (Brinker et al., 2018; Brinker & Jones, 2012). Although Credit Valley Conservation has
not used the the CCVI, they do use provincial status ranks produced by the Natural Heritage
Information Centre using the NatureServe Conservation Status tool. This pairs well with the
CCVI as the results can be added to the spreadsheet model and help provide more information
about a species. Based on CCVI’s wide use, as well as its ability to produce rapid assessments of
a large number of species, I will be using the tool to evaluate its use to CVC and aid in
communicating results to a wide audience that could lend to improved collaboration in future
projects. Collaboration in climate change planning and mitigation is key, and a more concerted
effort to do so is necessary.
The present study is a pilot project for CVC, to evaluate the CCVI and determine whether is
a useful tool to assist in climate change planning at a regional scale. Through this work, I expect
to obtain rankings of vulnerability for a set of the selected species. To achieve this and have
enough data, I have deliberately not chosen extremely rare species that may have limited
information in the published literature. Lastly, I anticipate that this work will enhance the
results of the Natural Systems Vulnerability in Peel Region report (Tu et al., 2017) since they did
not include such fine-scale traits in their assessment. In time, it would be beneficial for the work
to be expanded to include more species and taxonomic groups.
Through my research, I will provide recommendations on how CVC can contribute to
climate change research in southern Ontario. I will outline where this work might fit within
existing jurisdictions, and what roles different municipal tiers, and organizations have in
12
implementing climate change knowledge in practice. As CVC is currently working on a forest
management plan, it is pertinent to understand how to account for climate change in a more
concrete way, to ensure that a “business as usual” approach to management is not taken.
Objectives
The specific objectives of this project are to:
1. Conduct a climate change vulnerability assessment of forest plants within the Credit River watershed, using the CCVI tool;
2. Identify the key factors contributing to species’ vulnerabilities; 3. Use existing bioclimatic envelope models for several tree species within the Credit River
watershed and rankings from other CCVI projects, to support or dispute species ranks; and 4. Weigh the benefits and limitations of the CCVI tool to provide recommendations on how it
could be used by organizations like CVC in the future (i.e., by using existing Ecological Land Classification data to map vulnerable communities).
Methodology
The methodology used for the project was developed by NatureServe for the Canadian version 3.0 of the CCVI tool (Young et al., 2016). See Figure 2 for the process of conducting a VA using the CCVI. The CCVI framework includes direct and indirect climate exposures that contribute to an overall ranking (Fig. 3). The MS Excel (Microsoft, 2017) spreadsheet contains a total of 23 factors and one must score at least 13 of them in order to obtain a ranking (Young et al., 2016). The factors are distributed among four sections including; A) exposure to local climate change, B) indirect exposure to climate change, C) sensitivity and adaptive capacity, and D) documented or modelled response to climate change (optional). Each factor was ranked based on how it is expected to contribute to a species’ vulnerability (e.g. greatly increase, increase, somewhat increase, neutral). The CCVI is also meant to provide a starting point for identifying knowledge gaps related to climate change, as well as factors that contribute to species’ vulnerability.
Study area It is important to identify a discrete geographic area for an assessment using the CCVI
because climate raster data must be clipped to a specific geographic area in order for exposure scores to be calculated. The study area used to test the tool was defined as being any land within CVC’s jurisdiction (Fig. 1). The area of the Peel Region, that extends beyond CVC watershed, was not included due to limitations of data requirements on species occurrences.
13
Define geographic assessment area
Acquire species distribution data
Acquire or Calculate downscaled climate projection models
Compile species-specific sensitivity and life history
data
Perform GIS analyses to spatially overlay individual
species range files and spatially explicit climate
projection models
Assess life history data for required CCVI factors
Complete the CCVI excel worksheet
Interpret vulnerability score and class results
Figure 3. Direct and indirect climate exposure variables including sensitivity factors used to generate overall vulnerability scores in the Climate Change Vulnerability Index (Young et al., 2010)
Figure 2. The process of conducting a climate change vulnerability assessment using the NatureServe CCVI tool (modified from Glick et al., 2017)
14
Species selection As plants are primary producers, and drivers of community structure and composition, for
the purposes of this project, forest plants (trees, shrubs, grasses, forbs, ferns) were the focal taxonomic group. This was decided based on consultation with CVC staff and program directors, they need to have a better knowledge on how climate change could affect plant species and thus future forest management, restoration projects, and climate change mitigation actions.
Several different approaches were considered for species selection. In a recent study of species in the Ontario Great-Lakes basin (Brinker et al., 2018), species were selected based on several factors including habitat specialists or keystone species, species of conservation concern, and those which are at the southern or northernmost extremes in their range within the study area. Other studies have focused solely on at-risk species which poses an interesting issue. That is, rare and at-risk species can be vulnerable to climate change; however common species and those that are dominant in the ecosystem may also be vulnerable. The Ecological Land Classification (ELC) has been applied to characterize and map communities within CVC’s boundaries. ELC is based on the dominant vegetation and site (soil moisture and origin).
To select species that are representative of the ecological diversity that exists within the Watershed, several “filters” were applied. First, the dominant species in the ten most common, and ten least common Ecological Land Classification (ELC) vegetation types for forests and treed swamps were selected. This selection was based on which ELC types occupied the greatest or least area within the watershed boundaries. Additionally, any ELC types that were listed as provincially rare (S1-S3) by the NatureServe conservation status ranking system were included. This list was verified using data from the Integrated Watershed Monitoring Program (CVC, 2019), on species that occurred in 50% or more of sample plots within the watershed.
Species identified as “low-occurrence” with ten or fewer known occurrences in the watershed, were also included to represent more regionally rare species. To make the list more relevant for urban areas, data from the City of Mississauga OpenData (Mississauga, 2019) was used to determine the most abundant species of city-owned trees (street trees and those in city parks). Characteristic Carolinian tree species were also added, as they represent a small portion of the diversity in the lower watershed and have the potential to increase in abundance as the climate warms. Lastly, important invasive species were added to the list as CVC is interested in whether or not they will be favoured under future climate change.
A list of 124 species was compiled, and further reduced to 87 species based on feedback from individuals involved with the project. Those 87 species were then prioritized by how many selection filters applied to each. For example, a species may be present in one of the ten most common forest communities, be a dominant street tree in Mississauga, and be found in over 50% of the monitoring plots. The top 30 (Table 1) were chosen for the CCVI assessment.
15
Table 1. The list of species selected for CCVI assessment in the Credit watershed.
Species occurrence maps Species occurrence maps were created
in ArcMap 10.6.1 (ESRI, 2018) with assistance
from CVC’s Information Management division.
Data from CVC’s Natural Heritage database
(CVC, 2019), Bill McIlveen’s Dataset (2016),
Mississauga Natural Areas Assessment (NAS)
database (Mississauga, 2018), TEMO database
(CVC, 2019), and wetland database (CVC, 2019)
were used to create occurrence maps for each
species. All species occurrences, represented as
a point file, were projected to Universal
Transverse Mercator (UTM) points and further
used to map species occurrences. In addition to
these, species occurrences that had no specific
point location (no X and Y coordinates) were
identified by using the ELC polygons in which
the occurrence was documented. For these ELC
polygons, a centroid was identified and its
coordinates (in UTM projection) were used. All
observations were included to create an
accurate representation of the area of the
watershed in which a species can grow. Many
species have been observed in the entire
watershed; however, a few have only been
observed in the lower regions.
Climate data The dataset recommended by the CCVI guidelines is the ensemble of 15 general
circulation models (GCMs) from the International Panel on Climate Change (IPCC) 5th report.
The recommended data from the ClimateNA database is available through AdaptWest
(AdaptWest Project, 2015). In this case, the files were obtained from Bruce Young, of
NatureServe. The required raster data for this assessment includes future projections of annual
temperature change (in °C), and climate moisture deficit (Hamon AET:PET) for the 2050s with a
baseline period of 1961-1990. Secondly, historic data on temperature variation (in °C), and
mean annual precipitation (in mm), is required to determine past exposure to climate variation
within the watershed. The datasets are downscaled to 1-km resolution to encompass as much
Taxa Scientific Name Common Name
Fern Onoclea sensibilis Sensitive fern
Forb Allaria petiolate Garlic mustard
Arisaema triphyllum Jack-in-the-pulpit
Circaea canadensis Broad-leaved Enchanter's Nightshade
Glyceria Striata Fowl mannagrass
Impatiens capensis Spotted jewelweed
Rubus pubescens Dewberry
Shrub Cornus alternifolia Alternate-leaf dogwood
Cornus sericea Red-osier dogwood
Prunus virginiana Chokecherry
Rhamnus cathartica Common buckthorn
Tree Abies balsamea Balsam fir
Acer rubrum Red maple
Acer saccharum Sugar maple
Betula alleghaniensis
Yellow birch
Betula papyrifera Paper birch
Celtis occidentalis Common hackberry
Fagus grandifolia American beech
Fraxinus americana White ash
Fraxinus nigra Black ash
Fraxinus pennsylvanica
Green ash
Juglans nigra Black walnut
Ostrya virginiana Eastern hop-hornbeam
Prunus serotina Black cherry
Quercus rubra Red Oak
Sassafras albidum Sassafras
Thuja occidentalis Eastern white cedar
Tilia Americana American basswood
Tsuga canadensis Eastern hemlock
Ulmus americana American elm
16
fine-scale variation as possible. This raster data was then clipped to the watershed boundary,
using ArcMap 10.6 (ESRI, 2018), to determine historic and future exposure of species within its
boundary to changes in climate.
Species life history information Detailed life history information is required for each species which was obtained
through several sources. For most species, the US Forest Service Fire Effects Information
System (USDA, various dates), Silvics of North America (Burns & Honkala, 1990), Trees in
Canada (Farrar, 1995), and the Lady Bird Johnson Native Plant Database (Lady Bird Johnson
Wildflower Centre) were used. See Appendix C for a list of references that were used to provide
information that determined ranks for each species. The detailed guidelines (Young &
Hammerson, 2016) for the tool clearly outline how to rank each factor based on the species’
traits and should be followed closely in future assessments at CVC.
Mapping vulnerability of dominant tree species in ELC communities An example of a map that displays ELC communities dominated by species with differing
levels of vulnerability was created in ArcMap 10.6.1 (ESRI, 2018). The species selected to
illustrate this potential application of results were sugar maple, red oak, paper birch and balsam
fir. This was done after rankings were obtained for all selected species and represents
moderate, high, and extreme vulnerability. Should this type of mapping be done for the entire
watershed, one could then identify areas that have higher concentrations of vulnerable
communities and could prioritize management in those areas.
Results
Historic and future climate exposure The future climate conditions in the Credit River Watershed, and surrounding area
indicate an increased overall drying as indicated by positive values for the Hamon moisture
metric between 39-57 mm by 2050, compared to the baseline period (1961-1990) (Appendix A,
Fig. 1A). Net drying is expected to be greater in the southeastern portion of the watershed. An
increase in mean annual temperature between 3- 3.2 °C is also predicted (Appendix A, Fig. 1B).
Overall, the variation in predicted temperature increase is low within the study area, largely
due to the small spatial scale.
The data for RCP 4.5 to the 2050s shows that the Credit River Watershed, and
surrounding area has experienced moderate variation in precipitation (according to CCVI
guidelines) from 785-995 mm during the baseline period (1961-1990) (Appendix A, Fig. 1C).
Additionally, a mean seasonal temperature variation between 26.3-27.5 °C has been
experienced (Appendix A, Fig. 1D). Lower levels of precipitation have been observed in the
southeastern watershed, similar to the trend in predicted future drying. Similarly, slightly higher
17
temperature variation has also been recorded in the southernmost regions adjacent to Lake
Ontario (Appendix B, Table 2 shows detailed climate exposure scores for each species).
CCVI rankings Rankings of climate change vulnerability were derived from the CCVI for all species in
the assessment (Fig. 4, Table 3). There was enough information in the published literature and
plant guides to input at least 13 criteria for each species, excluding section D
(Documented/modeled response to climate change). The guidelines for the CCVI Canada V3.0
were closely followed to rank each factor based on the criteria described for each section.
Species ranked as “extremely vulnerable” include balsam fir, Eastern white cedar (Table
2). Species ranked “highly vulnerable” include jack-in-the-pulpit (Arisaema triphyllum [Linnaeus]
Schott), yellow birch (Betula alleghaniensis Britton), paper birch (Betula papyrifera Marshall),
American beech (Fagus grandifolia Ehrhart), white ash (Fraxinus americana Linnaeus), black ash
(Fraxinus nigra Marshall), fowl mannagrass (Glyceria striata [Lamarck] Hitchcock var. striata),
spotted jewelweed (Impatiens capensis Meerburgh), sensitive fern (Onoclea sensibilis
Linnaeus), Eastern hemlock (Tsuga canadensis [Linnaeus] Carrière), and American elm (Ulmus
americana Linnaeus). Species ranked “moderately vulnerable” include red maple (Acer rubrum
Linnaeus), sugar maple (Acer saccharum Marshall), alternate-leaved dogwood (Cornus
alternifolia Linnaeus f.), red-osier dogwood (Cornus s)ericea Linnaeus), green ash (Fraxinus
pennsylvanica Marshall), black walnut (Juglans nigra Linnaeus), Eastern hop-hornbeam (Ostrya
virginiana [Miller] K. Koch), black cherry (Prunus serotina Ehrhart), chokecherry (Prunus
EV7%
HV37%
MV43%
LV13%
Figure 4 proportions (%) of vulnerable species as ranked by the NatureServe CCVI tool (n=30). “LV” = less vulnerable, “MV” = moderately vulnerable, “HV” = highly vulnerable, and “EV” = extremely vulnerable
18
Table 2 Vulnerability rankings for each species, organized from most- to least- vulnerable.
RANKING CONFIDENCE COMMON NAME LATIN NAME
EXTREMELY VULNERABLE
Very high Balsam fir Abies balsamea
Low Eastern white cedar Thuja occidentalis
HIGHLY VULNERABLE
High Jack-in-the-pulipt Arisaema triphyllum
Very high Yellow birch Betula alleghaniensis
Very high Paper birch Betula papyrifera
Low American beech Fagus grandifolia
Very high White ash Fraxinus americana
Low Black ash Fraxinus nigra
Low Fowl mannagrass Glyceria Striata
High Spotted jewelweed Impatiens capensis
Low Sensitive fern Onoclea sensibilis
Very high Eastern hemlcok Tsuga canadensis
Very high American elm Ulmus americana
MODERATELY VULNERABLE
Very high Red maple Acer rubrum
High Sugar maple Acer saccharum
Very high Alternate-leaved dogwood Cornus alternifolia
High Red-osier dogwood Cornus sericea
Very high Green ash Fraxinus pennsylvanica
Moderate Black walnut Juglans nigra
Moderate Eastern hop-hornbeam Ostrya virginiana
High Black cherry Prunus serotina
Very high Chokecherry Prunus virginiana
Very high Red oak Quercus rubra
Moderate Dewberry Rubus pubescens
Very high Sassafras Sassafras albidum
Very high Basswood Tilia americana
LESS VULNERABLE
Very high Garlic mustard Allaria petiolata
Moderate Common hackberry Celtis occidentalis
Very high Broad-leaved enchanter’s nightshade
Circaea canadensis
Very high Common buckthorn Rhamnus cathartica
virginiana Linnaeus), red oak (Quercus rubra Linnaeus), dewberry (Rubus pubescens
Rafinesque), sassafras (Sassafras albidum [Nuttall] Nees), and basswood (Tilia americana
Linnaeus). Lastly, species ranked “less vulnerable” include garlic mustard (Alliaria petiolata [M.
Bieberstein] Cavara & Grande), common hackberry (Celtis occidentalis Linnaeus), broad-leaved
19
enchanter’s nightshade (Circaea canadensis [Linnaeus] Hill), and common buckthorn (Rhamnus
cathartica Linnaeus).
Factors most affecting vulnerability Similar to Brinker et al. (2018), I identified several factors contributed more to
vulnerability scores than others (Fig.5). Hydrological niche (both historical and physiological)
contributed to vulnerabilities in 93% and 33% of species, respectively. The presence or
anticipated presence of large-scale effects of pathogens or natural enemies (pests) was the
third most prevalent vulnerability factor at almost 27%, followed by species’ dispersal
capabilities (13%). The remaining factors listed affected the vulnerability of 6% and 3% of
species. Detailed rankings for each factor input into the CCVI can be found in Appendix B, Table
3.
CCVI rankings and other assessment areas Since the CCVI is an internationally-applicable tool, it makes it possible to compare and
apply results for species across multiple jurisdictional boundaries, creating a larger picture of
how vulnerability varies across a landscape. Several species that occur in the watershed and
that were ranked using the CCVI, have also been included in vulnerability assessments at larger
scale; the Ontario Great Lakes Basin (Brinker et al., 2018), and regions of West Virginia (Byers &
0 10 20 30 40 50 60 70 80 90 100
Historical hydrological niche
Physiological hydrological niche
Pathogens/ natural enemies
Dispersal/movement capabilities
Genetic bottleneck
Anthropogenic barriers
Physiological thermal niche
Genetic variation
Phenological response
percent of species affected (n=30)
CC
VI f
acto
r
Figure 3. Percentage contribution of factors to increased (“greatly increase”, “increase”, and “increase/somewhat increase”) climate change vulnerability of assessed species (n=30) in the Credit River watershed.
20
Table 3. CCVI rankings from this assessment and other studies in nearby geographic areas as well as species distribution modelling conducted for the Credit River Watershed. Green colouring indicates results that agree with what was found in the present study, orange indicates results that somewhat agree, red indicates results that do not agree.
Latin name
CCVI ranking
Brinker et al. (2018),
Penskar, 2014
Byers & Norris, 2011
Malcolm et al., 2008
Eastern hemlock (Tsuga canadensis) HV MV HV . Loser
sugar maple (Acer saccharum) MV LV . MV Stayer N
American beech (Fagus grandifolia) HV . HV . Stayer N
black ash (Fraxinus nigra) HV MV . . Loser
red maple (Acer rubrum) MV . . PS Stayer N
black cherry (Prunus serotina) MV . . MV Stayer
red oak (Quercus rubra) MV . . PS Stayer
Sassafras (Sassafras albidum) MV LV . . Winner
balsam fir (Abies balsamea) EV . . . Loser
Eastern white cedar (Thuja occidentalis) EV . EV . .
yellow birch (Betula alleghaniensis) HV . . . Loser
paper birch (Betula papyrifera) HV . . . Loser
American elm (Ulmus Americana) HV . . . Stayer
black walnut (Juglans nigra) MV . . .
Winner N/ Stayer S
basswood (Tilia Americana) MV . . . Stayer N
garlic mustard (Allaria petiolata) LV . . IL .
common hackberry (Celtis occidentalis) LV . . . Winner
Norris, 2011) and Michigan (Penskar, 2014). A study was also commissioned by CVC in 2007 to
create user-friendly mapping of tree species distribution changes under future climate
conditions for the Credit River Watershed (Malcom et al., 2008). Fifteen tree species overlap
with those in this assessment (Table 3). "losers"= species that were modelled to be currently
widely distributed in the Credit River Watershed, but whose habitat was projected to become
rare or absent in the future, "winners"= species that were modelled to be currently rare or
absent in the Credit River Watershed, but whose habitat was projected to become widely
21
distributed in the future, “stayer”= species that were projected to remain widely distributed in
the Credit River Watershed.
Mapping vulnerability by ELC type Once species vulnerability is assessed based on CCVI, I was able to map vegetation
community types dominated by four tree species, using data provided by CVC (2019) (Appendix
A, Fig. 2). Two areas are highlighted as they indicate higher concentrations of community types
dominated by vulnerable species. Additionally, the abundance of Sugar maple-dominated
communities is made apparent in the yellow-coloured ELC polygons which occur over a large
portion of the Watershed. Overall, sugar maple-dominant communities are abundant in the
watershed, occurring through the majority of the upper and middle watershed with some
presence in the lower. Red oak occurs in small pockets in the upper watershed, and in the lower
watershed close to Lake Ontario. Balsam fir and paper birch are both restricted to the upper
watershed and only in very small areas, as indicated by the inset map (Appendix A, Fig. 2).
Discussion
Climate results and uncertainty The results presented in the climate data provided by NatureServe align with those
projected for Peel Region in Auld et al. (2016), though the data were downscaled using slightly
different methods. However, the climate data provided by NatureServe provide a finer spatial
resolution (1 km) compared to the data for Peel Region (10 km) Auld et al. (2016). Regionally-
specific data at 1 km resolution are not widely available in Canada, although projects like
AdaptWest (AdaptWest Project, 2015) and ClimateData.ca (Canadian Centre for Climate
Services, 2019) are working to make it more accessible.
In addition to the uncertainty associated with different climate data sources, common
practice in work related to climate change is to consider multiple emissions scenarios (Deser et
al., 2012; Griffis-Kyle et al., 2018). In the case of this assessment only one, more conservative
scenario (RCP 4.5) was used to determine species’ future climate change exposure (IPCC, 2014).
The argument has been made that RCP 4.5 and the two more severe emissions scenarios (RCP
6.5 & 8.5) do not diverge until after 2050, which is the time frame used in this assessment
(Brinker et al., 2018; Young & Hammerson, 2016). However, to better account for the
uncertainty associated with climate change, a method more closely resembling that adopted by
Griffis-Kyle et al. (2018) should be adopted. The study compensated for the lack of accessible
data by calculating their own using downscaled CIMP5 data for monthly average surface air
temperature, potential evapotranspiration, and actual evapotranspiration, and three emissions
scenarios (RCP 2.6, RCP 6.0, and RCP 8.5) (Griffis-Kyle et al., 2018). Unfortunately, the required
22
data to replicate this methodology is not readily available in Canada, and thus, limited the
scope of this assessment.
On key example of why multiple scenarios and time periods should be used is because
some species may show relatively little response to changes in bioclimatic variables in most
scenarios, until one discrete time period that shows a sharp decline. Puric-Mladenovic et al.
(2011) assessed the vulnerabilities of terrestrial vegetation cover to climate change in the Lake
Simcoe Watershed using ecological niche modelling based on data from the “Canadian Climate
Model Version 2 climate scenarios for three time periods (2011-2040, 2041-2070 and 2071-
2100)”. Of several tree species assessed, sugar maple showed little response to climate change
in the 2020s and 2050s, however by the 2080s, shows a sharp decline in abundance across
most of southern Ontario (Puric-Mladenovic et al., 2011). Although the methodologies between
the aforementioned study and this CCVI assessment are different, it is important to consider
the potential overlap and to use both as indicators of species’ responses to climate change.
Factors affecting vulnerability/ rankings Several factors weighed more heavily in terms of affecting species’ vulnerability in the
watershed. This is to be expected as some were not as relevant for plant species, such as
dependence on ice or snow, uncommon geographic features, or diet. It is useful to understand
these factors when moving forward with further climate change-related studies as one can
begin to frame how to think about managing species for traits that will respond more
favourably to changes in climatic conditions. Several species were vulnerable due to a number
of factors. The vulnerability of blasam fir, for example, was increased due to its limited dispersal
abilities, low drought tolerance, and the impacts of two major pests (Appendix B, Table 3). In
addition, this species is at the southernmost portion of it’s range it the assessment area, further
decreasing it’s ability to adapt to changing conditions in the Credit River watershed. Some
species showed lower vulnerability including common buckthorn and garlic mustard. These two
invasives show impressive adaptive capacity through long-distance dispersal, their ability to
thrive in a variety of site conditions, and allelopathic properties that make them effective
competitors (Appendix B, Table 3). In general, factors that affect a species’ ability to thrive in
drought conditions, the effects of pathogens and pests, and the dispersal and movement
abilities of seeds greatly contribute to overall vulnerability.
Hydrological niche - Hydrological niche, both historical and physiological largely determine
where a species has been able to grow in the past and will continue to thrive into the future
(Silvertown et al., 2015). The CVC watershed has not experienced much variation in
precipitation compared to the baseline period, increasing the vulnerability of plants, according
to the CCVI guidelines, therefore vegetation communities may be more sensitive to drying in
the future. Because the remaining natural areas in the watershed are largely in riparian zones
and areas fed by groundwater, the vegetation is dominated by plants that are either dependent
on moist environments, or those that can tolerate wetter conditions in the event of flooding.
Eastern white cedar, American elm, ash species, balsam fir, Jewelweed, and Sensitive fern are
23
all examples of such species. Although it is difficult to generalize all of them, these species
typically show a preference for moist soil conditions and may be hydrologically sensitive to
drying (Burns & Honkala, 1990). Even though groundwater-fed ecosystems may be more
resilient in the face of climate change because they are likely to remain cooler, overall drying
will reduce the total area that has access to the water (Kundzewicz & Doll, 2009; Kurylyk et al.,
2013).
Pathogens and diseases - Several pathogens and diseases have already had a devastating impact
on plant species in Southern Ontario and the rest of Canada. Dutch Elm disease [Ophiostoma
ulmi (Buisman) Nannf.], Emerald ash borer [Agrilus planipennis Fairmaire], beech bark disease
(Neonectria faginata), and Hemlock woolly adelgid [Adelges tsugae Annand] have all worked to
weaken or completely extirpate natural populations of many tree species (Brunet & Guries,
2016; Eschtruth et al., 2006; NRCAN, 2016; Stephanson & Coe, 2017). Trees experiencing stress
from disease or pests are less likely to be able to withstand additional stressors associated with
climate change (Sturrock et al., 2011). The CCVI tool also accounts for diseases or pests that are
likely to reach the assessment area by the 2050s, which is important to consider, especially in
planning future tree planting activities in response to climate change. For example, Oak wilt
[Ceratocystis fagacearum (Bretz) Z.W. de Beer, Marinc., T.A. Duong and M.J. Wingf] has been
found in Michigan and is likely to reach southern Ontario despite best efforts to quarantine
contaminated wood and machinery (Jagemann, 2018; OFAH/OMNRF, 2012). This increases the
vulnerability of oak species, for example Pin oak [Quercus ellipsoidalis Hill] more southern-
adapted species. that may otherwise be relatively less vulnerable to climate warming. Similarly,
Laurel wilt [Raffaelea lauricola Harr., Fraedrich & Aghayeva] has impacted populations of
Sassafras in the United States and although it has not been found in regions that border
Ontario, warmer temperatures will likely expand the range of its vector, the Redbay ambrosia
beetle [Xyleborus glabratus Eichhof] (Formby et al., 2018). A commonly discussed option to
manage forests for climate change resilience is to increase planting of these more southerly-
adapted and Carolinian species (Almas & Conway, 2016; Taylor et al., 2006). It will be
increasingly important to take into consideration the diseases and pests that affect such species
in their native ranges, to develop proactive integrated pest management (IPM) programs.
Dispersal/ movements - The dispersal and movement capabilities of a species is something that
is unlikely to change and is related to the structure and quantity of seeds produced by a plant,
and method of seed dispersal (Young & Hammerson, 2016). This inherent dispersal ability is,
however, an important aspect of species that can contribute to their ability to migrate and
colonize new areas under future climate change (Young & Hammerson, 2016). Species that rely
on abiotic factors for dispersal, such as tree species that produce winged seeds that are wind-
dispersed, are generally scored less vulnerable in this category than those that rely on a small
group of species for dispersal (Young & Hammerson, 2016). These types of seeds, however, do
not typically travel more than 100 m from the source, unless carried in an updraft above the
forest canopy which is a highly unpredictable activity (Horn et al., 2001). In contrast, species
24
that have propagules that are carried by small mammals or birds can be found over 1 km from
the source (Conlisk et al., 2012). This phenomenon has been attributed to the population
persistence of Engelman oak [Quercus engelmannii Greene] in California and will likely
contribute to the persistence of Oak species in southern Ontario (Conlisk et al., 2012). The main
concern with plants that rely on a small number of species for dispersal is that if those
dispersers also experience population declines related to climate change, habitat loss and
fragmentation, or other factors. Thus vulnerability of dispersers and their ability disperse plants
in fragmented landscapes can have negative impacts on plant dispersal (Pearson & Dawson,
2005; Travis et al., 2013). In general, plants that can disperse greater distances will likely be
more resilient in the face of climate change as populations are not as isolated as those with
more limited dispersal abilities (Travis et al., 2013).
Genetic variation - The genetic variation of natural populations of any organism, including plants
contributes to the potential adaptive capacity of that species (Young et al.,2016). Since climate
change pushes systems beyond thresholds which they have previously experienced, forests
with greater genetic diversity will respond better to changes in abiotic conditions outside the
range that species are naturally adapted to (Alfaro et al., 2014). Phenotypic plasticity can only
go so far to allow individuals to respond to changes in climatic factors, and long-term health of
populations will rely on the ability of a species to adapt and migrate (Alfaro et al., 2014).
Several species in this assessment have reportedly low genetic variation including balsam fir,
spotted jewelweed, sensitive fern, eastern hemlock, and eastern white cedar (Klekowski &
Lloyd, 1968; Pandey & Rajora, 2012; Potter et al., 2012; Shes & Furnier, 2002; Toczydlowski &
Waller, 2019). Eastern white cedar reportedly shows much lower genetic variation than other
closely-related species Thuja plicata Donn and T. orientali Linnaeus, due to high rates of selfing
via vegetative reproduction (Pandey & Rajora, 2012). A similar observation has been made in
Balsam fir populations where the genetic variability is lower than most other conifers (Shes &
Furnier, 2002), and Eastern hemlock which has experienced large-scale inbreeding (Potter et al.,
2012).
Groups of interest
Spring ephemerals - Spring ephemerals are species of wildflower that bloom early in the spring,
before the forest canopy has closed, prior to leaf-out of many dominant tree species (Meier et
al., 1995). Jack-in-the pulpit was the only spring ephemeral species assessed in this project
however a recent study shows that this group of species may have already experienced 40%
decline in the last decade (Heberling et al., 2019). By measuring first leaf-out date (FLD) of the
canopy, and first flowering date (FFD) of jack-in-the-pulpit and comparing results to similar data
collected in Concord, Massachusetts in the late 1800s, they were able to quantify changes in
phenology. It was found that FLD has responded to increased spring temperatures by occurring
earlier in the spring, whereas FFD has been less responsive, thus narrowing the window of time
during which the spring ephemerals can photosynthesize. This has likely reduced the carbon
25
budget of these wildflowers to the tune of approximately 25%, leading to projected declines in
population of 10-48% (Heberling et al., 2019). Similar studies have not been conducted in
Ontario, however it could be hypothesized that similar impacts could be observed if measured.
This example of phenological mismatch has not been studied until recently and highlights a
larger issue in climate change science that is, it is nearly impossible to understand the
widespread effects of climate change on all taxonomic groups and we are likely missing many
fine-scale details about how phenology of forest plants is affected.
Invasive species - Understanding the impacts of climate change on invasive species will be a
crucial part of management planning (IUCN, 2017; Panda et al., 2018). Garlic mustard and
Common buckthorn were both ranked “LV” in this assessment which does not provide a very
encouraging view of the future condition of natural areas under climate change. Of course, the
exact characteristics that make these species invasive, those that allow them to rapidly colonize
new areas, and to withstand a variety of microclimatic conditions, also prove to be useful when
faced with increased temperature and droughts (Ren et al., 2009). One factor that could pose a
threat to invasive species in general is low genetic variation due to founder effects whereby a
small subset of a population is introduced to a new area, effectively reducing the genetic
variation of that “founding” population (Mullarkey et al., 2013). The origins of Garlic mustard
and Common buckthorn into North America show evidence of multiple introductions (Durka et
al., 2005; Kurylo & Endress, 2012; Meekins et al., 2001), therefore the species may be less
affected and have relatively higher genetic variation in local populations. In forests already
experiencing stress from climate change, disease, and fragmentation, invasive species may
become more prolific as we see changes in the forest canopy (Dukes et al., 2009).
Comparing results to previous work The results from the three studies as previously indicated in Table 3 that also used the
CCVI in relatively nearby geographic regions both agree and disagree with those found in this
assessment. Surprisingly, every species that overlapped with Brinker et al. (2018) were ranked
one step higher (more vulnerable) in the current assessment. It is likely the case that
vulnerability is scale-dependent- a result of different exposure scores at different scales. It is
also possible that variation in the literature review portion of the assessment and the individual
creating the rankings causes individual differences in species ranks. In this case it is more likely
the difference in spatial scale because when the exposure scores from that study were input
into the rankings for this assessment (everything else being the same), Sugar maple was ranked
“LV”, the same as in the Great Lakes study. This shortcoming of CCVI has been noted by other
researchers using the tool and has been addressed by using occurrence data at different spatial
scales and averaging the numeric scores for vulnerability (Griffis-kyle et al., 2018).
Three species overlapped with a study conducted in Michigan (Penskar, 2014) including
American beech, Eastern white cedar, and hemlock. All three rankings were the same as those
in this assessment which is encouraging as the study areas are not adjacent, however closely
26
related in terms of climate. The study that took place in West Virginia, however, did not
produce results that were unanimously in agreement (Byers & Norris, 2011). This is not
surprising as the geographic area is farther southwest than the Credit River Watershed.
The results of bioclimatic envelope modelling for the watershed conducted by Malcolm
et al. (2008), ranked species according to how the suitable habitat is expected to change under
future climate conditions, according to data from the third Coupled Intercomparison Project
(CIMP3), though since the time of this study new models have become available ( e.g. CIMP5 is
available). This does not take away from the fact that the ranges of several species are expected
to shift farther North and become reduced in the watershed (Malcolm et al., 2008). The species
that are deemed “losers” in Malcolm et al (2008) include Eastern hemlock, black ash, balsam fir,
paper birch, and yellow birch- all species that were ranked “HV” or “EV” in this assessment.
Generally, those ranked as “stayers” were scored “MV” which is interesting as the ranges may
not be expected to change much, although functional traits and future climate exposure may
work reduce the fitness of those species in the Watershed.
Mapping vulnerable communities The results of ELC mapping do not, by any means, provide a comprehensive enough idea of
the distribution of vulnerable communities. This will, however, prove to be useful contingent
Figure 6 Density of Lake Ontario Basin species found to be extremely or highly vulnerable to climate change as part of an assessment of the Ontario Great Lakes Basin (Brinker et al., 2018).
27
upon more detailed assessments in the future. In Ontario, Brinker et al. (2018), mapped the
density of vulnerable species across watersheds within the Great Lakes Basin (Fig. 6). This
approach is useful as it illustrates potential areas of concern and focus for further planning.
Since the study area was small for the current assessment, and CVC is a unique entity with its
own objectives and data, I was able to map vulnerability of four dominant tree species utilizing
information from the ELC. Should a similar process be adopted for all dominant vegetation, a
more comprehensive picture of how species vulnerability is distributed across the landscapes
could be developed.
Benefits and limitations Through testing and using the tool, I have been able to develop a list of benefits and
limitations of the CCVI and its use in broader applications (Table 4). These align closely with
what other users have identified (Young et al., 2015), and should be considered when moving
forward with future assessments at CVC, and other organizations in southern Ontario.
Table 4. Summary of the benefits and limitations of the Climate Change Vulnerability Index (CCVI) tool.
Benefits Limitations
✓ Accessible ✓ Easy-to-use ✓ Allows for rapid assessment of many
species ✓ A good first step to incorporate climate
change into natural heritage management
✓ Repeatable methodology that can be applied at various spatial scales
o Allows for communication across jurisdictional boundaries
✓ Allows for the expansion of projects to cover multiple taxa
✓ Can be used to identify areas of higher vulnerability when combined with spatial data
o Use in restoration planning ✓ Integration with species distribution
models is possible
▪ Provides simplified picture of climate change
o Only accounts for two bioclimatic variables
o Does not account for periodically wetter conditions due to increases in flooding
▪ Difficult to assess species under multiple climate change scenarios
o NatureServe can only provide climate data for RCP 4.5 in Canada
▪ Does not provide accommodation for species with multiple niches
▪ Results can be taken “at face value” without further analysis
▪ Gives the illusion of “doing something” but may not actually lead to implementation
▪ Susceptible to personal bias as criteria and rankings can seem arbitrary at times
o I am not an expert!
28
Recommendations
Future vulnerability assessments 1. Rank each species under multiple climate change scenarios- this is dependent on the
accessibility of downscaled climate data and may not be possible, however it should be
done. It is important to at least acknowledge the need to do so and indicate that the
required data is not available yet.
2. Conduct more detailed assessments based on groups of species- continuing the work
of mapping ELC communities according to vulnerability. This would also allow for a more
comprehensive study of vulnerable traits within each and to determine what species
may be good candidates for restoration and introduction. It would also create better-
organized reports and could be interpreted more easily by readers.
3. Work with other organizations- to identify roles and responsibilities in implementing
climate change into planning in southern Ontario. The MNRF should likely assume the
role of conducting larger-scale VAs and work with smaller organizations including
conservation authorities to implement targeted plans, or smaller-scale assessment.
Other work 4. Develop a plan for assisted migration of species with more southerly seed zones and
introducing more Carolinian species into natural areas to increase biodiversity. This is
imperative to ensure that decreased regeneration of native populations that can be
anticipated by climate change does not result in reduced overall canopy. There is a real
chance that invasive species will become more prevalent in the future as they are able
to take advantage of stressed out forest ecosystems.
5. Conduct a study on FLD and FFD of spring ephemerals (similar to Heberling et al., 2019)
to determine whether similar trends that have been found in Connecticut can be
observed in the Watershed.
6. Develop public education campaigns- to engage the general public on how climate
change is anticipated to affect natural heritage in the watershed. For example, a
campaign to explain how climate change is affecting populations of spring ephemeral
flowers would trigger an emotional response in people that could be leveraged to
increase advocacy for improved planning for climate change in natural areas.
Conclusion Upon completing this vulnerability assessment, several conclusions were drawn from
the process of using the CCVI and the species results. Most species in this assessment were
ranked as being vulnerable to climate change and the factors that contributed most to that
vulnerability were the hydrological niche, and past changes in precipitation, the presence of
pathogens or pests, dispersal and movement capabilities of seeds, any history of genetic
29
bottlenecks and low genetic variation, phenology, anthropogenic barriers, and thermal niche.
None of the species in this assessment are provincially or regionally rare, which highlights the
need to continue to view species through a lens that is unique to climate change. Traditional
conservation status rankings are not sufficient at identifying the threat of climate change to
natural heritage. The results from this study align with those found in similar assessments,
although different spatial scales likely contribute to differing vulnerability rankings, caused by
less severe exposure scores. Results can be applied spatially by imposing species vulnerabilities
onto ELC mapping communities dominated by species with varying degrees of vulnerability.
This may be useful as a coarse-scale filter to prioritize restoration, assisted migration, and
monitoring activities in the future.
The CCVI has its benefits in that it is generally easy-to-use, accessible, and does not
require much technical expertise beyond basic GIS skills to calculate exposure scores. Since this
has already been completed for CVC, future assessments using this tool will not need to go
through this step. Moving forward, a more concerted effort must be made to develop datasets
for multiple climate change scenarios, tailored to use with the CCVI so that assessors can better
incorporate the uncertainty of climate change into their work. The CCVI is a useful tool to
develop a basic understanding of how climate change may impact species in the future, and to
identify key areas to focus further research and work. In the coming years, I expect more
people will begin using this tool and it will be important to move ahead with clear goals for
managing natural areas for climate change resilience.
30
References
AdaptWest Project. (2015). Gridded current and projected climate data for North America at 1km resolution,
interpolated using the ClimateNA v5.10 software (T. Wang et al., 2015)
Akbari, H., Pomerantz, M., & Taha, H. (2001). Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy, 70(3): 295–310.
Alfaro, R. I., Fady, B., Vendramin, G. G., Dawson, I. K., Fleming, R. A., Sáenz-Romero, C., … Loo, J. (2014). The role of forest genetic resources in responding to biotic and abiotic factors in the context of anthropogenic climate change. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2014.04.006
Almas, A. D., & Conway, T. M. (2016). The role of native species in urban forest planning and practice: A case study of Carolinian Canada. Urban Forestry and Urban Greening. https://doi.org/10.1016/j.ufug.2016.01.015
Anacker, B. L., Gogol-Prokurat, M., Leidholm, K., & Schoenig, S. (2013). Climate Change Vulnerability Assessment of Rare Plants in California. Madroño, 60(3): 193-210.
Association of Fish and Wildlife Agencies (AFWA). (2012). The state of state fish and wildlife climate adaptation: summary report of the 2012 AFWA State Adaptation Activity Survey. Association of Fish and Wildlife Agencies, Washington, D.C., USA.
Auld, H., Switzman, H., Comer, N., Eng, S., Hazen, S., & Milner, G. (2016). Climate trends and future projections in the Region of Peel. Ontario Climate Consortium: Toronto, ON: pp.103.
Austin, M. P., & Van Niel, K. P. (2011). Improving species distribution models for climate change studies: Variable selection and scale. Journal of Biogeography, 38(1): 1–8.
Bagne, K.E., Friggens, M. M., and Finch, D. M. (2011). A System for Assessing Vulnerability of Species (SAVS) to Climate Change. Gen. Tech. Rep. RMRS-GTR-257. Fort Collins, CO. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 28 p.
Brinker, S.R., Garvey, M., & Jones, C.D. (2018). Climate change vulnerability assessment of species in the Ontario Great Lakes Basin. Ontario Ministry of Natural Resources and Forestry, Science and Research Branch, Peterborough, ON. Climate Change Research Report CCRR-48. 85 p. + append.
Brinker, S.R., & Jones, C.D. (2012). The vulnerability of provincially rare species (species-at-risk) to climate change in the Lake Simcoe watershed, Ontario, Canada. Natural Heritage Information Centre, Science and Information Branch, Ontario Ministry of Natural Resources, Peterborough, ON. Climate Change Research Report CCRR-31
Brunet, J., & Guries, R.P. (2016). Elm genetic diversity and hybridization in the presence of Dutch elm disease. Proceedings of the American elm restoration workshop 2016, American elm ecology.
Burns, R.M., and Honkala, B.H. (1990). Silvics of North America: 2. Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service, Washington, DC. vol. 2, 877 p.
Byers, E. & Norris, S. (2011). Climate change vulnerability assessment of species of concern in West Virginia. Project Report. West Virginia Division of Natural Resources, Elkins, West Virginia.
31
Canadian Centre for Climate Services. (2019). ClimateData.ca. Environment and Climate Change Canada. Retrieved from https://climatedata.ca/
Climate Change Sensitivity Database (CCSD). 2012. Climate Change Sensitivity Database. http://climatechangesensitivity.org/.
Conlisk, E., Lawson, D., Syphard, A.D., Franklin, J., Flint, A., & Regan, H.M. (2012). The roles of dispersal, fecundity, and predation in the population persistence of an Oak (Quercus engelmannii) under global change. PLoS ONE, 7(5), e36391.
Credit Valley Conservation (2009). Credit valley conservation rising to the challenge: a handbook for understanding and protecting the Credit river watershed.
Deser, C., Phillips, A., Bourdette, V., & Teng, H. (2012). Uncertainty in climate change projections: The role of internal variability. Climate Dynamics. https://doi.org/10.1007/s00382-010-0977-x
Dukes, J. S., Pontius, J., Orwig, D., Jeffrey, R. G., Vikki, L. R., Brazee, N … & Ayres, M. (2009). Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of northeastern North America: What can we predict? Canadian Journal of Forest Research. https://doi.org/10.1139/X08-171
Durka, W., Bossdorf, O., Prati, D., & Auge, H. (2005). Molecular evidence for multiple introductions of garlic mustard (Allaria petiolata) to North America. Molecular Ecology, 14(6), 1697-1706.
Eschtruth, A.K., Cleavitt, N.L., Battles, J.J., Evans, R.A., & Fahey, T.J. (2006). Vegetation dynamics in declining eastern hemlock stands: 9 years of forest response to hemlock woolly adelgid infestation. Canadian Journal of Forest Research, 36, 1435-1450.
Feenstra, J. F., Burton, I., Smith, J. B., & Tol, R. S. J. (1998). Handbook on Methods for Climate Change Impact Assessment and Adaptation Strategies. UNEP; Vrije Universiteit Amsterdam.
Formby, J.P., Rodgers, J.C. III, Koch, F.H., Krishnan, N., Duerr, D.A., & Riggins, J.J. (2018). Cold tolerance and invasive potential of the redbay ambrosia beetle (Xyleborus glabratus) in the eastern United States. Biological Invasions, 20, 995-1007.
Füssel, H. M., & Klein, R. J. T. (2006). Climate change vulnerability assessments: An evolution of conceptual thinking. Climatic Change, 75(3): 301–329.
Glick, P., Stein, B. A., & Edelson, N. A. (2011). Scanning the Conservation Horizon. National Wildlife Federation.
Greene, C. S., & Millward, A. A. (2017). Getting closure: The role of urban forest canopy density in moderating summer surface temperatures in a large city. Urban Ecosystems, 20(1): 141–156.
Griffis-Kyle, K. L., Mougey, K., Vanlandeghem, M., Swain, S., & Drake, J. C. (2018). Comparison of climate vulnerability among desert herpetofauna. Biological Conservation, 225: 164- 175.
Harrington, T. C., Fraedrich, S. W., & Achayeva, D. N. (2008). Raffaelea lauricola, a new ambrosia beetle symbiont and pathogen on the Lauraceae. Mycotaxon, 104: 399-404.
Heberling, J. M., McDonough MacKenzie, C., Fridley, J. D., Kalisz, S., & Primack, R. B. (2019). Phenological mismatch with trees reduces wildflower carbon budgets. Ecology Letters. https://doi.org/10.1111/ele.13224
32
Hijmans, R. J., & Graham, C. H. (2006). The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology, 12(12): 2272–2281.
Horn, H. S., Nathan, R., & Kaplan, S. R. (2001). Long-distance dispersal of tree seeds by wind. Ecological Research. https://doi.org/10.1046/j.1440-1703.2001.00456.x
IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
IUCN. (2017). Issues brief: Invasive alien species and climate change. https://www.iucn.org/sites/dev/files/ias_and_climate_change_issues_brief_final.pdf
Jagemann, S.M., Juzwik, J., Tobin, P.C., & Raffa, K.F. (2018). Seasonal and regional distributions, degree-day models, and phoresy rates of the major sap beetle (Coleoptera: nutidulidae) vectors of the oak wilt fungus, Bretziella fagacearum, in Wisconsin. Environmental Entomology, 47(5), 1152-1164.
Klekowski, E.J. Jr., & Lloyd, R.M. (1968). Reproductive biology of the Pteridophyta 1. General consdiderations and a study of Onoclea sensibilis L. The Journal of the Linnean Society. Botany, 60(383), 315-324.
Kundzewicz, Z. W., & Döll, P. (2009). Will groundwater ease freshwater stress under climate change? Hydrological Sciences Journal. https://doi.org/10.1623/hysj.54.4.665
Kurylo, J., & Endress, A. (2012). Rhamnus cathartica: Notes on its early history in North America. Northeastern Naturalist, 19(4), 601-610.
Kurylyk, B. L., P.-A. Bourque, C., & Macquarrie, K. T. B. (2013). Potential surface temperature and shallow groundwater temperature response to climate change: An example from a small forested catchment in east-central NB (Canada). Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-17-2701-2013
Liebezeit, J., E. Rowland, M. Cross, and S. Zack. (2012). Assessing limate change vulnerability of breeding birds in Arctic Alaska. A report prepared for the Arctic Landscape Conservation Cooperative. Wildlife Conservation Society, North America Program, Bozeman, MT., 167pp.
Malcolm, J., Kramm, D., Puric-Mladenovic, D., & Shi, H. (2008). Projected tree distributions in the Credit Valley
Conservation Authority under global warming. 30 pp, Unpublished. Faculty of Forestry, University of Toronto, Ontario.
Meekins, J.F., Ballard, H.E.Jr., & McCarthy, B.C. (2001). Genetic variation and molecular biogeography of a North
American invasive plant species (Allaria petiolata, Brassicaceae). International Journal of Plant Science, 162(1): 161-169.
Meier, A.J., Bratton, S.B., & Duffy, D.C. (1995). Possible ecological mechanisms for loss of vernal-herb diversity in
logged eastern deciduous forests. Ecological Applications, 5, pp. 935-946
Moriondo, M., Giannakopoulos, C., & Bindi, M. (2011). Climate change impact assessment: The role of climate extremes in crop yield simulation. Climatic Change, 104: 679–701.
Mullarkey, A. A., Byers, D. L., & Anderson, R. C. (2013). Inbreeding depression and partitioning of genetic load in the invasive biennial Alliaria petiolata (Brassicaceae). American Journal of Botany. https://doi.org/10.3732/ajb.1200403
33
OFAH/OMNRF Invading Species Awareness Program. (2012). Oak Wilt. Retrieved from: www.invadingspecies.com.
Pacifici, M., Foden, W. B., Visconti, P., Watson, J. E. M., Butchart, S. H. M., Kovacs, K. M., … Rondinini, C. (2015). Assessing species vulnerability to climate change. Nature Climate Change, 5(3): 215–225.
Panda, R. M., Behera, M. D., & Roy, P. S. (2018). Assessing distributions of two invasive species of contrasting habits in future climate. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2017.12.053
Pandey, M. & Rajora, O.P. (2012). Higher fine-scale genetic structure in peripheral than in core populations of a long-lived and mixed-mating conifer- eastern white cedar (Thuja occidentalis L.). BMC Evolutionary Biology, 12(48).
Pearson, R. G., & 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. (2005). Long-distance plant dispersal and habitat fragmentation: Identifying conservation targets for spatial landscape planning under climate change. Biological Conservation. https://doi.org/10.1016/j.biocon.2004.12.006
Penskar, M.R. & Derosier, A.L. (2013). Assisting the Michigan Wildlife Action Plan: Tools and Information for Incorporating Plants. Final Report to NatureServe. Michigan Natural Features Inventory Report No. 2013-03, Lansing, MI. 26 pp. + appendices.
Phillips, S.J. (2017). A Brief Tutorial on Maxent. Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2019-12-15.
Potter, K.M., Jetton, R.M., Dvorak, W.S., Hipkins, V.D., Rhea, R., & Whittier, W.A. (2012). Widespread inbreeding and unexpected geographic patterns of genetic variation in eastern hemlock (Tsuga canadensis), an imperiled North American conifer. Conservation Genetics, 12, 475-498.
Puric-Mladenovic, D., Malcolm, J., She, H., Strobl, S., & Buck, J. (2011). An analysis of terrestrial ecosystems/ vegetation cover to climate change in the Lake Simcoe watershed. Ontario Centre for Climate Impacts and Adaptation Resources.
Razgour, O., Forester, B., Taggart, J. B., Bekaert, M., Juste, J., Ibáñez, C., … Manel, S. (2019). Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1820663116
Rempel, R. S., & Hornseth, M. L. (2017). Binational climate change vulnerability assessment of migratory birds in the Great Lakes Basins: Tools and impediments. PLoS ONE, 12(2): 12pp.
Ren, M. X., & Zhang, Q. G. (2009). The relative generality of plant invasion mechanisms and predicting future invasive plants. Weed Research. https://doi.org/10.1111/j.1365-3180.2009.00723.x
Schierenbeck, K. A. (2017). Population-level genetic variation and climate change in a biodiversity hotspot. Annals of Botany. https://doi.org/10.1093/aob/mcw214
34
Shen, M., Chen, J., Zhuan, M., Chen, H., Xu, C. Y., & Xiong, L. (2018). Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2017.11.004
Shes, K., & Furnier, G. (2002). Genetic variation and poulation structure in central and isolated populations of Balsam Fir, Abies balsamea. American Journal of Botany, 89(5), 783-791.
Sievert, N. A., Paukert, C. P., Tsang, Y. P., & Infante, D. (2016). Development and assessment of indices to determine stream fish vulnerability to climate change and habitat alteration. Ecological Indicators, 67: 403-416.
Silvertown, J., Araya, Y., & Gowing, D. (2015). Hydrological niches in terrestrial plant communities: A review. Journal of Ecology. https://doi.org/10.1111/1365-2745.12332
Sinclair, S. J., M. D. White, & G. R. Newell. (2010). How useful are species distribution models for managing biodiversity under future climates? Ecology and Society 15(1): 8.
Stein, B.A., Glick, P., Edelson, N., & A. Staudt (eds.). (2014). Climate-Smart Conservation: Putting Adaptation Principles into Practice. National Wildlife Federation, Washington, D.C.
Stephanson, C.A., & Coe, N.R. (2017). Impacts of beech bark disease and climate change on american beech.
Forests, 8(5), 155.
Still, S. M., Frances, A. L., Treher, A. C., & Oliver, L. (2015). Using two climate change vulnerability assessment methods to prioritize and manage rare plants: a case study. Natural Areas Journal, 35(1): 106–121.
Sturrock, R. N., Frankel, S. J., Brown, A. V., Hennon, P. E., Kliejunas, J. T., Lewis, K. J., Worrall, J.J., & Woods, A. J. (2011). Climate change and forest diseases. Plant Pathology. https://doi.org/10.1111/j.1365-3059.2010.02406.x
Taylor, M. E., Gray, P. A., & Schiefer, K. (2006). Helping Canadians adapt to climate change in the Great Lakes coastal zone. Great Lakes Geographer, 13(1), 14-25.
Toczydlowski, R.H., & Waller, D.M. (2019). Drift happens: Molecular genetic diversity and differentiation among populations of jewelweed (Impatiens capensis Meerb.) reflect fragmentation of floodplain forests. Molecular ecology, 28(10), 2459-2475.
Travis, J. M. J., Delgado, M., Bocedi, G., Baguette, M., Bartoń, K., Bonte, D., Boulangeat, I., HIdgson, J.A>, Kubisch, A., Penteriani, V., Saastamoinen, M., Stevens, V.M., & Bullock, J. M. (2013). Dispersal and species’ responses to climate change. Oikos. https://doi.org/10.1111/j.1600-0706.2013.00399.x
Tu, C., Milner, G., Lawrie, D., Shrestha, N., Hazen, S. (2017). Natural systems vulnerability to climate change in Peel Region. Technical Report. Toronto, Ontario: Toronto and Region Conservation Authority and Ontario Climate Consortium Secretariat.
U.S. Environmental Protection Agency (EPA). (2009). A framework for categorizing the relative vulnerability of threatened and endangered species to climate change. National Center for Environmental Assessment, Washington, DC; EPA/600/R-09/011.
35
Wang, T., Hamann, A., Spittlehouse, D. L., & Murdock, T. Q. (2012). ClimateWNA-high-resolution spatial climate data for western North America. Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/JAMC-D-11-043.1
Willis, S. G., Foden, W., Baker, D. J., Belle, E., Burgess, N. D., Carr, J. A., … Butchart, S. H. M. (2015). Integrating climate change vulnerability assessments from species distribution models and trait-based approaches. Biological Conservation, 190: 167–178.
Araya, Y. N., Silvertown, J., Gowing, D. J., McConway, K. J., Peter Linder, H., & Midgley, G. (2011). A fundamental, eco-hydrological basis for niche segregation in plant communities. New Phytologist. https://doi.org/10.1111/j.1469-8137.2010.03475.x
Young, B. E., Dubois, N. S., & Rowland, E. L. (2015). Using the climate change vulnerability index to inform adaptation planning: Lessons, innovations, and next steps. Wildlife Society Bulletin, 39(1): 174–181.
Young, B. E., E. Byers, K. Gravuer, K. R. Hall, G. A. Hammerson, and A. Redder. (2016). Guidelines for Using the NatureServe Climate Change Vulnerability Index Release 3.0-Canada. NatureServe, Arlington, Virginia, USA.
Young, B. E., & Hammerson, G.A. (2016). Guidelines for Using the NatureServe Climate Change Vulnerability Index. NatureServe, Arlington, Virginia, USA.
36
Appendix A: Figures
Figure 1 Climate data used to calculate species' exposure to climate change in the Climate Change Vulnerability Index (CCVI) A) Projected future drying (Hamon AET:PET) by 2050 in the study area (RCP 4.5) B) Projected future temperature change (C) by 2050 (RCP 4.5) C) Historic mean annual precipitation (mm) for the baseline period (1961-1990) D) Historic mean seasonal temperature for the baseline period (1961-1990)
37
Figure 2 communities mapped based on the ecological land classification system (ELC) in the Credit River watershed, characterized by either Paper birch (HV), Red oak (MV), Sugar maple (MV), or Balsam fir (EV)- dominant. Climate Change Vulnerability Index.
38
Appendix B: Tables
Table 1 modified from Lankford et al. (2014) shows the comparison of the major input requirements for the NatureServe Climate ChangeVulnerability Index-Canadian release v3.0 (NSCCVI), U.S. Forest Service System for Assessing the Vulnerability of Species (SAVS), the Climate Change Sensitivity Database (CCSD), and The Environmental Protection Agency Framework for Categorizing the Relative Vulnerability of Threatened and Endangered Species to Climate Change
Assessment title
Major inputs
Type of tool
Uncertainty
score
Types of species
Geographic
areas covered
Author
SAVS Habitat
Physiology
Phenology
Biotic interactions
Online
questionnaire
yes Terrestrial
vertebrates
Canada and the U.S. US forest
service (Bagne
et al. 2011)
NSCCVI- Canada
v3.0
Downscaled change in
temperature
(Climate Wizard 2050s) and the
Moisture availability
(Hamon AET:PET,
Climate Wizard 2050s)
Current species distribution
Physiological, phenological
species traits
Knowledge of natural and
anthropogenic barriers
Documented adaptation
responses
MS Excel
spreadsheet
yes Terrestrial and
aquatic, all
taxonomic groups
Canada and the U.S. NatureServe
(Young et al.
2015)
CCSD Generalist versus specialist
Physiology
Life history traits
Habitat sensitivities
Dispersal ability
Disturbance regime
Ecological interactions
Database yes Terrestrial and
aquatic, all
taxonomic groups
Pacific
Northwest Canada and U.S.
University of
Washington/
Nature
Conservancy
(2012)
A Framework for
Categorizing the
Relative
Vulnerability of
Threatened and
Endangered Species
to Climate Change
Current population size
Population trend in the last 50
years
Current population trend
Range trend in the last 50 years
Current range trend
Current non-climatic stressors
Likely current stressor future
trends
Individual replacement time
Future vulnerability to
stochastic events
Future vulnerability to policy/
management changes
Future vulnerability to natural
stressors
Physiological traits
Framework yes Terrestrial and
aquatic, all
taxonomic groups
US
Environmental
Protection
Agency (EPA,
2009)
39
Table 2 Future climate conditions for each species, within the Credit River Watershed. Numbers represent the percent of pixels within the species range that fall into each category for change in temperature (C) and moisture (Hamon AET:PET)
Temperature Scope Climate Moisture Deficit Scope
Species English Name >3.8C 3.49-3.8C
3.17-3.48C
2.85-3.16C
2.53-2.84C <2.53C >56.68
38.87-56.68
21.05-38.86
3.23-21.04
-14.59-3.22 <-14.59
Abies balsamea Balsam Fir 0 0 0.32 99.68 0 0 0.1 99.9 0 0 0 0
Acer rubrum Red Maple 0 0 0.32 99.68 0 0 0.11 99.89 0 0 0 0
Acer saccharum Sugar Maple 0 0 0.37 99.63 0 0 0.1 99.9 0 0 0 0
Allaria petiolata Garlic Mustard 0 0 0.32 99.68 0 0 0.1 99.9 0 0 0 0
Arisaema triphyllum Jack-in-the-Pulpit 0 0 0.3 99.7 0 0 0.1 99.9 0 0 0 0
Betula alleghaniensis Yellow Birch 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Betula papyrifera Paper Birch 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Celtis occidentalis Common Hackberry 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Circaea canadensis Broad-Leaf Enchanter's Nightshade 0 0 0.32 99.68 0 0 0.1 99.9 0 0 0 0
Cornus alternifolia Alternate-Leaf Dogwood 0 0 0.32 99.68 0 0 0.1 99.9 0 0 0 0
Cornus sericea Red-Osier Dogwood 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Fagus grandifolia American Beech 0 0 0.32 99.68 0 0 0.1 99.9 0 0 0 0
Fraxinus americana White Ash 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Fraxinus nigra Black Ash 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Fraxinus pennsylvanica Green Ash 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Glyceria Striata Fowl mannagrass 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Impatiens capensis Spotted jewelweed 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Juglans nigra Black Walnut 0 0 0.23 99.77 0 0 0.12 99.88 0 0 0 0
Onoclea sensibilis Sensitive Fern 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Ostrya virginiana Ironwood 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Prunus serotina Black Cherry 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Prunus virginiana Chokecherry 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
40
Quercus rubra Northern Red Oak 0 0 0.32 99.68 0 0 0.1 99.9 0 0 0 0
Rhamnus cathartica Common Buckthorn 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Rubus pubescens Dewberry 0 0 0.33 99.67 0 0 0.1 99.9 0 0 0 0
Sassafras albidum Sassafras 0 0 0 100 0 0 0 100 0 0 0 0
Tilia americana American Basswood 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Tsuga canadensis Eastern Hemlock 0 0 0.33 99.67 0 0 0.1 99.9 0 0 0 0
Tuja occidentalis Eastern White Cedar 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Ulmus americana American Elm 0 0 0.31 99.69 0 0 0.1 99.9 0 0 0 0
Table 3 Scores for each factor of the Climate Change Vulnerability Index (CCVI) by species, in aplphabetical order by Latin name (N= “neutral”, U= “unknown”, SI= “somewhat increase”, Inc= “increase”, GI= “greatly increase”)
Sp
ecie
s
Sea l
evel
Natl
ba
rrie
rs
An
th b
arr
iers
La
nd
Use C
ha
ng
e
Dis
pe
rsal/M
ovem
en
t
His
t. t
he
rmal n
ich
e
Ph
ysio
l. t
he
rmal n
ich
e
His
t. h
yd
rol. n
ich
e
Ph
ysio
l. h
yd
rol. n
ich
e
Dis
turb
an
ce
Ice/s
no
w
Un
co
mm
on
Geo
Fe
atu
res
Oth
er
sp
p f
or
hab
Die
t
Po
llin
ato
rs
Oth
er
sp
p d
isp
Path
og
en
s/e
ne
mie
s
Co
mp
eti
tio
n
Oth
er
inte
rsp
ecif
ic
inte
racti
on
Gen
eti
c v
ar
Gen
bo
ttle
ne
ck
Rep
rod
uc
tive s
yste
m
Ph
en
ol
resp
on
se
Abies balsamea N N N U Inc SI Inc Inc Inc SI-N N N N N/A N N Inc U N SI N/A N/A N
Acer rubrum N N SI-N U SI SI N Inc SI N N U N N/A N N SI N U N N/A N/A SI-N
Acer saccharum N N N U SI SI N Inc SI-N SI-N N N N N/A N N U N U N N/A N/A SI-N
Allaria petiolata N N N U SI-N SI N Inc N N N N N N/A N N N U N N N/A N/A N
Arisaema triphyllum N N SI-N U SI-N SI SI Inc SI SI N N N N/A SI N U N U U U N Inc-SI
Betula alleghaniensis N N N U SI SI SI Inc Inc N N U N N/A N N Inc U N U N N/A U
Betula papyrifera N N SI U Inc SI N Inc N N N U N N/A N N Inc SI-N N N N/A N/A N
Celtis occidentalis N N SI U SI-N SI N SI N U N N N N/A N N N N N SI-N N/A N/A N
Circaea canadensis N N N U SI SI U Inc N N N N N N/A N N N U N N N/A N/A U
Cornus alternifolia N N N U N SI N Inc N U N N N N/A N N U SI-N N U N N/A Inc
41
Cornus sericea N N SI-N U SI-N SI U Inc SI N N N N N/A N N N N N N N/A N/A SI-N
Fagus grandifolia N N N U SI SI N Inc SI N N N N N/A N Inc-SI Inc N N N N/A N/A U
Fraxinus americana N N SI-N U SI SI N Inc SI U N N N N/A N N Inc N U U Inc N/A U
Fraxinus nigra N N SI-N U SI SI N Inc Inc N N U N N/A N N Inc SI N U Inc N/A U
Fraxinus pennsylvanica N N SI U SI SI N Inc N U N N N N/A N N SI N U U SI N/A U
Glyceria Striata N N SI-N U SI SI SI Inc Inc N N U N N/A N N U SI N U N N/A U
Impatiens capensis N N SI-N U GI-Inc SI SI-N Inc Inc U N N N N/A N N N N N Inc-SI N/A N/A U
Juglans nigra N N SI U SI-N SI U Inc SI-N N N U N N/A N Inc-SI SI U N N N/A N/A U
Onoclea sensibilis N N SI U N SI Inc-SI Inc Inc U N N N N/A N N N N N SI-N N/A N/A U
Ostrya virginiana N N N U Inc-SI SI N Inc SI-N N N N N N/A N N N N N U N N/A U
Prunus serotina N N SI U SI-N SI SI-N Inc N SI-N N N N N/A N N U SI-N N N N/A N/A U
Prunus virginiana N N SI U N SI N Inc N N N N N N/A N N SI N U U N N/A U
Quercus rubra N N SI U SI-N SI N Inc N N N N N N/A N N Inc-SI N N N N/A N/A U
Rhamnus cathartica N N N U N SI N Inc N N N N N N/A N N N N N SI-N N/A N/A U
Rubus pubescens N N N U Inc-SI SI U Inc Inc SI N N N N/A N N U N N U U SI-N U
Sassafras albidum N N Inc U SI SI N GI N N N N N N/A N N SI N U U U U U
Tilia americana N N N U SI SI U Inc SI SI N N N N/A N N U N N N N/A N/A N
Tsuga canadensis N N SI U Inc SI U Inc Inc N N N N N/A N N Inc N N SI N/A N/A U
Tuja occidentalis N N SI U Inc SI Inc-SI Inc Inc SI N N N N/A N N N N N SI N/A N/A U
Ulmus americana N N SI U SI SI U Inc Inc SI N N N N/A N N Inc N N U SI-N N/A N
42
Appendix C: List of references used to obtain CCVI rankings Archetti, M., Richardson, A.D., O'Keefe, J., & Delpierre, N. (2013). Predicting climate change impacts on the amount and duration of autumn
colors in a New England forest. PLoS ONE, 8(3), e57373. Battles, J.J., Cleavitt, N.L., Saah, D.S., Poling, B.T., & Fahey, T.J. (2017). Ecological impact of a microburst windstorm in a northern hardwood
forest. Canadian Journal of Forest Research, 47, 1695-1701. Beaubien, E.G., & Freeland, H.J. (2000). Spring phenology trends in Alberta, Canada: links to ocean temperature. International Journal of
Biometeorology, 44, 53-59.
Beier, G.L., Hokanson, S.C., Bates, S.T., & Blanchette, R.A. (2015). Aurantioporthe corni gen. et comb. nov., an endophyte and pathogen of
Cornus alternifolia. Mycologia, 107(1), 66-79.
Bierzychudek, P. (1982). The demography of Jack-in-the-pulpit, a forest plant that changes sex. Ecological Monographs, 52(4), 335-351.
Bolton, N., Shannon, J., Davis, J., Van Grinsven, M., Jin Noh, N., Schooler, S., Kolka, R., Pypker, T., & Wagenbrenner, J. (2018). Methods to improve survival and growth of planted alternative species seedlings in Black Ash ecosystemes threatened by Emerald Ash Borer. Forests, 9, 146.
Borkowski, D.S., Hoban, S.M., Chatwin, W., & Romero-Severson, J. (2017). Rangewide population differentiation and population substructure in
Quercus rubra L. Tree Genetics & Genomics,13, 67.
Bose, A.K., Weiskittel, A., & Wagner, R.G. (2017). A three decade assessment of climate-associated changes in forest composition across the
north-eatern USA. Journal of Applied Ecology, 54, 1582-1604.
Bourgeois, B., Vanasse, A., & Poulin, M. (2016). Effects of competition, shade and soil conditions on the recolonization of three forest herbs in tree-planted riparian zones. Applied Vegetation Science, 19, 679-688.
Bourque, C.P.A., Cox, R.M., Allen, D.J. Arp, P.A., & Meng, F-R. (2005). Spatial extent of winter that events in eastern North America: historical
weather records in relation to yellow birch decline. Global Change Biology, 11, 1477-1492.
Brunet, J., & Guries, R.P. (2016). Elm genetic diversity and hybridization in the presence of dutch elm disease. Proceedings of the American elm
restoration workshop 2016, American elm ecology.
Brunet, J., Zalapa, J., Guries, R. (2016). Conservation of genetic diversity in slippery elm (Ulmus rubra) in Wisconsin despite the devastating
impact of Dutch elm disease. Conservation Genetics, 17, 1001-1010.
Burns, R.M., & Honkala, B.H. tech. coords. (1990). Fraxinus Americana in Silvics of North America: 2. Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service, Washington, DC. vol. 2, 877 p.
Burns, R.M., and Honkala, B.H. tech. coords. (1990). Silvics of North America: 2. Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service, Washington, DC. vol. 2, 877 p.
Carey, J.H. (1993). Thuja occidentalis. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/thuocc/all.html [2019, November 5].
Carey, J.H. (1993). Tsuga canadensis. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, Fire Sciences Laboratory (Producer). Available:
https://www.fs.fed.us/database/feis/plants/tree/tsucan/all.html [2020, January 16].
Coladonato, M. (1991). Fagus grandifolia. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, Fire Sciences Laboratory (Producer). Available:
https://www.fs.fed.us/database/feis/plants/tree/faggra/all.html [2019, November 5].
Coladonato, M. (1991). Juglans nigra. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/jugnig/all.html [2019, November 17].
43
Coladonato, M. (1992). Ulmus americana. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, Fire Sciences Laboratory (Producer). Available:
https://www.fs.fed.us/database/feis/plants/tree/ulmame/all.html [2020, January 16].
Coladonato, M. (1994). Cornus alternifolia. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/shrub/coralt/all.html [2019, November 5].
Conlisk, E., Lawson, D., Syphard, A.D., Franklin, J., Flint, A., & Regan, H.M. (2012). The roles of dispersal, fecundity, and predation in the population persistence of an Oak (Quercus engelmannii) under global change. PLoS ONE, 7(5), e36391.
Crowder, W., Geyer, W.A., & Broyles, P.A. (2003). Chokecherry plant guide. United States Department of Agriculture. Retrieved from:
https://www.nrcs.usda.gov/Internet/FSE_PLANTMATERIALS/publications/kspmcpg5596.pdf
Čufar, K., De Luis, M., Saz, M.A., Črepinšek, Z., Kajfež-Bogataj, L. (2012). Temporal shifts in leaf phenology of beech (Fagus sylvatica) depend on elevation. Trees, 26(4), 1091-1100.
Darris, D. (2005). Plant fact sheet for fowl mannagrass (Glyceria striata). USDA-Natural Resources Conservation Service, Plant Materials Center, Corvallis, OR.
De Grabdpere, L., Gagnon, D., & Bergeron, Y. (1993). Changes in the understory of Canadian southern boreal forest after fire. Journal of
Vegetation Science, 4, 803-810.
De Kort, H., Mergeay, J., Jacquemyn, H., & Honnay, O. (2016). Transatlantic invasion routes and adaptive potential in North American populations of Glossy Buckthorn, Frangula alnus. Annals of Botany, 118, 1089-1099.
Delcourt, H.R., & Delcourt, P.A. (1994). Postglacial rise and decline of Ostrya virginiana (Mill.) K. Koch and Carpinus caroliniana Walt. In Eastern North America: predictable responses of forest species to cyclis changes in seasonality of climates. Journal of Biogeography, 21(2), 137-150.
D'Orangeville, L., Coˆte´, B., Houle, D., Morin, H., & Duchesne, L. (2013). A three-year increase in soil temperature and atmospheric N deposition has minor effects on the xylogenesis of mature Balsam Fir. Trees, 27, 1525-1536.
Dukes, J.S., Pontius, J., Orwig, D., Garnas, J.R., Rodgers, V.L., Brazee, N., Cooke, B., Theoharides, K.A., STange, E.E., Harrington, R., Ehrenfeld., J.,
Gurevitch, J., Lerdau, M., Stinson, K., Wick, R., & Ayres, M. (2009). Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of Northeastern North America: what can we predict?. Canadian Journal of Forest Research, 39, 231-248.
Durka, W., Bossdorf, O., Prati, D., & Auge, H. (2005). Molecular evidence for multiple introductions of garlic mustard (Allaria petiolata) to North America. Molecular Ecology, 14(6), 1697-1706.
Erdmann, G. G. (1990). Betula alleghaniensis Britton yellow birch. In: Burns, Russell M.; Honkala, Barbara H., technical coordinators. Silvics of North America. Volume 2. Hardwoods. Agric. Handb. 654. Washington, DC: U.S. Department of Agriculture, Forest Service: 133-147.
Eschtruth, A.K., Cleavitt, N.L., Battles, J.J., Evans, R.A., & Fahey, T.J. (2006) Vegetation dynamics in declining eastern hemlock stands: 9 years of
forest response to hemlock woolly adelgid infestation. Canadian Journal of Forest Research, 36, 1435-1450.
Fang, J., & Lechowicz, M.J. (2006). Climatic limits for the present distribution of beech (Fagus L.) species in the world. Journal of Biogeography,
33, 1804-1819.
Farrar, J.L. (1995). Trees in Canada. Natural Resources Canada, Canadian Forest Service, Ottawa, Copublished by Fitzhenry and Whiteside Limited, Markham, Ontario. 502 p.
Fernando, W.G.D., Zhang, J.X., Chen, C.Q., Remphrey, W.R., Schurko, A., & Klassen, G.R. (2005). Molecular and morphological characteristics of
Apiosporina morbosa, the causal agent of black knot in Prunus spp. Canadian journal of Plant Pathology, 27, 364-375.
Filewood, B., & Thomas, S.C. (2013). Impacts of a spring heat wave on canopy processes in a northern hardwood forest. Global Change Biology, 20(2).
44
Formby, J.P., Rodgers, J.C. III, Koch, F.H., Krishnan, N., Duerr, D.A., & Riggins, J.J. (2018). Cold tolerance and invasive potential of the redbay
ambrosia beetle (Xyleborus glabratus) in the eastern United States. Biological Invasions, 20, 995-1007.
Gauthier, M-M., & Jacobs, D. (2011), Walnut (Juglans spp.) ecophysiology in response to environmental stress and potential acclimation to climate change. Annals of Forest Science, 68, 1277-1290.
Ghelardini, L., Pepori, A.L., Luchi, N., Capretti, P., & Santini, A. (2016). Drivers of emerging fungal diseases of forest trees. Forest Ecology and
Management, 381, 235-246.
Gram, W.K., & Sork, V.L. (2001). Association between environmental and genetic heterogeneity in forest tree populations. Ecology, 82(7), 2012-2021.
Grant, J.F., Windham, M.T., Haun, W.G., Wiggins, G.J., & Lambdin, P.L. (2011). Initial assessment of thousand canker disease on Black Walnut, Juglans nigra, in Eastern Tennessee. Forests, 2, 741-748.
Green, S.R., Arthur, M.A., & Blankenship, B.A. (2010). Oak and red maple seedling survuval and growth following periodic prescribed fire on
xeric ridgetops on the Cumberland Plateau. Forest Ecology and Management, 259(12), 2256-2266.
Gucker, Corey L. 2005. Fraxinus nigra. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/franig/all.html [2019, November 17].
Gucker, Corey L. (2011). Celtis occidentalis. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, Fire Sciences Laboratory (Producer). Available:
https://www.fs.fed.us/database/feis/plants/tree/celocc/all.html [2020, January 16].
Gucker, Corey. (2012). Cornus sericea. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/shrub/corser/all.html [2019, November 17].
Gunter, L.E., Tuskan, G.A., Gunderson, C.A., & Norby, R.J. (2000). Genetic variation and spatial structure in sugar maple (Acer saccharum
Marsh.) and implications for predicted global-scale environmental change. Global Change Biology, 6, 335-344. Haber, E. (1976). Circeaea X intermedia in eastern North America with particular reference to Ontario. Canadian Journal of Botany, 55, 2919-
2935 Hadziabdic, D., Fitzpatrick, B.M., Wang, X., Wadl, P.A., Rinehart, T.A., Ownley, B.H., Windham, M.T., & Trigiano, R.N. (2010). Analysis of genetic
diversity in flowering dogwood natural stands using microsatellites: the effects of dogwood anthracnose. Genetica, 138(9-10), 1047-
1057.
Hadziabdic, D., Wang, X., Wadl, P.A., Rinehart, T.A., Ownley, B.H., & Trigiano, R.N. (2012). Genetic diversity of flowering dogwood in the Great
Smoky Mountains National Park. Tree Genetics & Genomes, 8(4), 855-871.
Hawkins, C.D.B., & Dhar, A. (2012). Spring bud phenology of 18 Betula papyrifera populations in British Columbia. Scandinavian Journal of Forest Research, 27, 507-519.
Hayashi, M., Feilich, K.L., & Ellerby, D.J. (2009). The mechanistics of explosive seed dispersal in orange jewelweed (Impatiens capensis). Journal of Experimental Botany, 60(7), 2045-2053.
Heberling, J.M., MacKenzie, C.M., Fridley, J.D., Kalisz, S., & Primack, R.B. (2019). Phenological mismatch with trees reduces wildflower carbon
budgets. Ecology Letters, 22(4), 616-623.
Heimonen, K., Valtonen, A., Kontunen-Soppela, S., Keski-Saari, S., Rousi, M., Oksanen, E., & Roininen, H. (2017). Susceptibility of silver birch (Betula pendula) to herbivorous insects is associated with the size and phenology of birch- implications for climate warming. Scandinavian Journal of Forest Research, 32(2), 95-104.
Hennigar, C.R., MacLean, D.A., Quiring, D.T., & Kershaw, J.A.Jr. (2008). Difference in Spruce Budworm defoliation among Balsam Fir and White,
Red, and Black Spruce. Forest Science, 54(2), 158-166.
Heschel, M.S., & Riginos, C. (2005). Mechanisms of selection for drought stress tolerance and avoidance in Impatiens capensis (Balsaminaceae). American Journal of Botany, 92(1), 37-44.
45
Hilty, J. (2009). Fraxinus Americana (White ash) in: trees, shrubs, and woody vines of Illinois. Retrieved from: https://www.illinoiswildflowers.info/trees/plants/white_ash.html
Hilty, J. (2009). Sassafras albidum (Sassafras) in: trees, shrubs, and woody vines of Illinois. Retrieved from:
https://www.illinoiswildflowers.info/trees/plants/sassafras.htm Horn, H.S., Nathan, R., & Kaplan, S.R. (2001). Long-distance dispersal of tree seeds by wind. Ecological Research, 16, 877-885.
Housset, J.M., Girardin, M.P., Baconnet, M., Carcaillet, C., & Bergeron, Y. (2015). Unexpected warming-induced growth decline in Thuja
occidentalis at its northern limits in North America. Journal of Biogeography, 42, 1233-1245. Iverson, L., Knight, K.S., Prasad, A., Herms, D.A., Matthews, S., Peters, M., Smoth, A., Hartzler, D.M., Long, R., &Almendinger, J. (2016). Potential
species replacements for Black Ash (Fraxinus nigra) at the confluence of two threats: Emerald Ash Borer and a changing climate. Ecosystems, 19, 248-270.
Jagemann, S.M., Juzwik, J., Tobin, P.C., & Raffa, K.F. (2018). Seasonal and regional distributions, degree-day models, and phoresy rates of the
major sap beetle (Coleoptera: nutidulidae) vectors of the oak wilt fungus, Bretziella fagacearum, in Wisconsin. Environmental
Entomology, 47(5), 1152-1164.
Johnson, W.C., & Adkisson, C.S. (1985). Dispersal of Beech nuts by Blue Jays in fragmented landscapes. The American Midland Naturalist,
113(2), 319-324.
Keir, K.R., Bemmels, J.B., & Aitken, S.N. (2011). Low genetic diversity, moderate local adaptation, and phylogenetic insights in Cornus nuttallii
(Cornaceae). American Journal of Botany, 98(8), 1327-1336.
Kirsch, E.M., & Wellik, M.J. (2017). Tree species preferences of foraging songbirds during spring migration in floodplain forests of the upper
Mississippi river. The American Midland Naturalist, 177, 226-249.
Klekowski, E.J. Jr., & Lloyd, R.M. (1968). Reproductive biology of the Pteridophyta 1. General considerations and a study of Onoclea sensibilis L. The Journal of the Linnean Society. Botany, 60(383), 315-324.
Kuddes-Fischer, L.M., & Arthur, M.A. (2002). Response of understory vegetation and tree regeneration to a single prescribed fire in Oak-Pine forests. Natural Areas Journal, 22, 43-52.
Kurylo, J., & Endress, A. (2012). Rhamnus cathartica: Notes on its early history in North America. Northeastern Naturalist, 19(4), 601-610.
Landscape Ontario. (n.d.), Quercus ellipsoidalis Northern Pin Oak, Hill's Oak from: Morton Arboretum Salt Tolerant Trees. Michigan State
University Extension Service Publication, HM-95
Liang, L. (2015). Geographic variations in spring and autumn phenology of white ash in a common garden. Physical Geography, 36(6), 489-509.
Lind-Riehl, J., & Gailing, O. (2015). Fine-scale spatial genetic structure of two red oak species, Quercus rubra and Quercus ellipsoidalis. Plant
Systematics and Evolution, 301, 1601-1612.
Logan, S.A., Phuekvilai, P., Sanderson, R., & Wolf, K. (2019). Reproductive and population genetic characteristics of leading-edge and central
populations of two temperate forest tree species and implications for range expansion. Forest Ecology and Management, 433, 475-
486.
Makela, M., Michael, P., Theriault, G., & Nkongolo, K.K. (2016). High genetic variation among closely related red oak (Quercus rubra)
populations in an ecosystem under metal stress: analysis of gene regulation. Genes & Genomics, 38, 967-976.
Martin, J.A., Sobrino-Plata, J., Rodriguez-Calcerrada, J., Collada, C., & Gil, L. (2019). Breeding and scientific advances in the fight against Dutch
elm disease: Will they allow the use of elms in forest restoration?. New Forests, 50, 183-215.
McCarthy, D.M., & Mason-Gamer, R.J. (2016). Chloroplast DNA-based phylogeography of Tilia americana (Malvaceae). Systematic Botany,
41(4), 865-880.
McEuen, A.B., & Curran, L.M. (2004). Seed dispersal and recruitment limitation across spatial scales in temperate forest fragments. Ecology, 85(2), 507-518.
Meekins, J.F., Ballard, H.E.Jr., & McCarthy, B.C. (2001). Genetic variation and molecular biogeography of a North American invasive plant species (Allaria petiolata, Brassicaceae). International Journal of Plant Science, 162(1): 161-169.
46
Missouri Botanical Garden. (n.d.). Onoclea sensibilis in: Plant finder. http://www.missouribotanicalgarden.org/PlantFinder/PlantFinderDetails.aspx?kempercode=l300
Moore, J.E., & Swihart, R.K. (2006). Nut selection by captive Blue Jays: importance of availability and implications for seed dispersal. The
Condor, 108(2), 377-388.
Muilenburg, V.L., &Herms, D.A. (2012). A review of Bronze Birch Borer (Coleoptera: Buprestidae) lif history, ecology, and management. Environmnetal Entomology, 41(6), 1372-1385.
Munger, Gregory T. (2001). Alliaria petiolata. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/forb/allpet/all.html [2019, November 17].
Natural Resources Canada. (2019). Emerald Ash Borer. Retrieved from https://www.nrcan.gc.ca/our-natural-resources/forests-forestry/wildland-fires-insects-disturban/top-forest-insects-diseases-cana/emerald-ash-borer/13377
Natural Resources Canada. (2015). Cypress Tip Moth in: Trees, insects and diseases of Canada’s forests. Retrieved from
https://tidcf.nrcan.gc.ca/en/insects/factsheet/12106
Natural Resources Canada. (2015). Red Maple in: Trees, insects and diseases of Canada’s forests. Retrieved from:
https://tidcf.nrcan.gc.ca/en/trees/factsheet/84
Natural Resources Canada. 2017. Acer Saccharum in: Plant hardiness of Canada. Retrieved from http://www.planthardiness.gc.ca/index.pl?speciesid=9000053&phz=wrcp4510000532041-2070&lang=en&m=24m
Nilsson, S.G. (1985). Ecological and evolutionary interactions between reproduction of beech Fagus sylvatica and seed eating animals. Oikos,
44, 157-164.
Norby, R.J., Hartz-Rubin, J.S., & Verbrugge, M.J. (2003). Phenological responses in maple to experimental atmospheric warming and CO2 enrichment. Global Change Biology, 9, 1792-1801.
Northern Ontario Plant Database. (n.d.). Plant description, Arisaema triphyllum. Retrieved from
http://www.northernontarioflora.ca/description.cfm?speciesid=1001483
OFAH/OMNRF Invading Species Awareness Program. (2012). Thousand Cankers Disease. Retrieved from: www.invadingspecies.com.
Oswald, E.M., Pontius, J., Rayback, S.A., Schaberg, P.G., Wilmont, S.H., Dupigny-Giroux, L-A. (2018). The complex relationship between climate and sugar maple health: climate change implications in Vermont for a key northern hardwood species. Forest Ecology and Management, 422, 303-312.
Pandey, M. & Rajora, O.P. (2012). Higher fine-scale genetic structure in peripheral than in core populations of a long-lived and mixed-mating
conifer- eastern white cedar (Thuja occidentalis L.). BMC Evolutionary Biology, 12(48).
Parker, M.A. (1987). Pathogen impact on sexual vs. asexual reproductive success in Arisaema triphyllum. American Journal of Botany, 74(11),
1758-1763.
Pettenkofer, T., Burkardt, K., Ammer, C., Vor, T., Finkeldey, R., Muller, M., Krutovsky, K., Vornam, B., Leinemann, L., & Gailing, O. (2019). Genetic
diversity and differentiation of introduced red oak (Quercus rubra) in Germany in comparison with reference native North American
populations. European Journal of Forest Research, 138, 275-285.
Pope, K.S., Dose, V., Da Silva, D., Brown, P.H., Leslie, C.A., & Dejong, T. (2013). Detecting nonlinear response of spring phenology to climate change by Bayesian analysis. Global Change Biology, 19, 1518-1525.
Potter, K.M., Jetton, R.M., Dvorak, W.S., Hipkins, V.D., Rhea, R., & Whittier, W.A. (2012). Widespread inbreeding and unexpected geographic
patterns of genetic variation in eastern hemlock (Tsuga canadensis), an imperiled North American conifer. Conservation Genetics,
12, 475-498.
Primack, R.B., & Miao, S.L. (1992). Dispersal can limit local plant distribution. Conservation Biology, 6(4), 513-519.
47
Pureswaran, D.S., De Grandpre, L., Pare, D., Taylor, A., Barrette, M., Morin, H., Regniere, J., & Kneeshaw, D.D. (2015). Climate-induced changes in host tree-insect phenology may drive ecological state shift in boreal forests. Ecology, 96(6), 1480-1491.
Redlin, S.C., & Rossman, A.Y. (1991). Cryptodiaporthe cornni (Diaporthales), cause of Cryptodia or the canker of Pagoda dogwood. Mycologia,
83(2), 200-209.
Régnière, J., V. Nealis, and K. Porter. 2009. Climate suitability and management of the gypsy moth invasion into Canada. Biological Invasions.
11: 135–148.
Saeki, I., Dick, C.W.D., Barnes, B.V., & Murakami, N. (2011). Comparative phylogeography of red maple (Acer rubrum L.) and silver mape (Acer
saccharinum L.): impacts of haitat specialization, hybridization and glacial history. Journal of Biogeography, 38, 992-1005.
Schultz, J. (n.d.). Jack-in-the-Pulpit in: Plant of the week. United States Department of Agriculture. Retrieved from:
https://www.fs.fed.us/wildflowers/plant-of-the-week/arisaema_triphyllum.shtml
Schwartz, M.W., & Heim, J. (1996). Effects of a prescribed fire on degraded forest vegetation. Natural Areas Journal, 16(3), 184-191.
Seltzner, S., & Eddy, T.L. (2003). Allelopathy in Rhamnus cathartica, European Buckthorn. The Michigan Botanist, 42, 51-61.
Shannon, J., Van Grinsven, M., Davis, J., Bolton, N., Jin Noh, N., Pypker, T., & Kolka, R. (2018). Water level controls on sap flux of canopy species in Black Ash Wetlands. Forests, 9, 147.
Shes, K., & Furnier, G. (2002). Genetic variation and poulation structure in central and isolated populations of balsam fir, Abies balsamea.
American Journal of Botany, 89(5), 783-791. Smithberg, M.H., & Weiser, C.J. (1968). Patterns of variation among climatic races of Red-osier dogwood. Ecology, 49(3), 495-505. Stapanian, M.A., & Smith, C.C. (1986). How Fox Squirrels influence the invasion of prairies by nut-bearing tree. American Society of
Mammalogists, 67(2), 326-332.
Stephanson, C.A., & Coe, N.R. (2017). Impacts of beech bark disease and climate change on American Beech. Forests, 8(5), 155.
Stevens, K.A., Woeste, K., Chakraborty, S., Crepeau, M.W., Leslie, C.A., Martinez-Garcia, P.J., Puiu, D., Romero-Severson, J., Coggeshall, M., Dandekar, A.M., Kluepfel, D., Neale, D.B., Salzberg, S.L., & Langley, C.H. (2018). Genomic variation among and within six Juglans species. Genes Genomics Genetics, 8, 2153-2165.
Sullivan, Janet. (1993). Sassafras albidum. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/sasalb/all.html [2019, October 23].
Sullivan, Janet. (1994). Betula alleghaniensis. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/betall/all.html [2019, November 17].
Tanino, K.K., Cherry, K.M., Kriger, J.N., Hrycan, W., Marufu, G., Thomas, J.D., & Gray, G.R. (2014). Photosynthetic responses to temperature-
mediated dromancy induction in contrasting ecostypes of red-osier dogwood (Cornus sericea L.). Environmental and Experimental Botany, 106, 221-230.
Tardif, J., & Bergeron, Y. (1993). Radial growth of Fraxinus nigra in a Canadian boreal floodplain in response to climatic and hydrological
fluctuations. Journal of Vegetation Science, 4, 751-758.
Teskey, R., Wertin, T., Bauweraerts, I., Ameye, M., McGuire, M.A., &Steppe, K. (2015). Responses of tree species to heat waves and extreme heat events. Plant, Cell and the Environment, 38, 1699-1712.
Tirmenstein, D. A. (1991). Acer saccharum. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/acesac/all.html [2019, October 23]
Tirmenstein, D. A. (1991). Quercus rubra. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/querub/all.html [2020, January 16].
48
Toczydlowski, R.H., & Waller, D.M. (2019). Drift happens: Molecular genetic diversity and differentiation among populations of jewelweed (Impatiens capensis Meerb.) reflect fragmentation of floodplain forests. Molecular ecology, 28(10), 2459-2475.
Tomiczek, C. (n.d.). American Thuja shoot moth, Thuja mining moth- Argyresthia thuiella in: Diseases and prests of trees in urban environments. Retrieved from: http://www.stadtbaum.at/sdata/322_engl.htm
Tooke, F., & Battey, H. (2010). Temperate flowering phenology. Journal of Experimental Botany, 61(11), 2853-2862.
Trigiano, R.N., Hadziabdic, D., Mantooth, K., Windham, M.T., Ownley, B.H., Staton, M.E., Miller, S., & Zhang, N. (2018). Dogwood anthracnose:
The story continues. Acta Horticulturae, 1191(1191): 77-82.
Uchytil, Ronald J. (1991). Betula papyrifera. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/betpap/all.html [2019, November 17].
Uchytil, Ronald J. (1991). Prunus serotina. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/tree/pruser/all.html [2019, November 17].
University of Kentucky. (2020). Pin oak. University of Kentucky, Department of Agriculture. Retrieved from:
https://www.uky.edu/hort/sites/www.uky.edu.hort/files/pages-attachments/QPALUSprint.pdf
Verburg, R., Maas, J., & During, H.J. (2000). Clonal diversity in differently-aged populations of the pseudo-annual clonal plant Circaea lutetiana L. Plant Biology, 2, 646-652.
Victory, E.R., Glaubitz, J.C., Rhodes, O.E.Jr., & Woeste, K.E. (2006). Genetic homogeneity in Juglans nigra (Juglandaceae) at nuclear
microsatellites. American Journal of Botany, 93(1), 118-126.
Vitt, P., Holsinger, K.E., & Jones, C.S. (2003). Local differentiation and plasticity in size and sex expression in Jack-in-the-Pulpit, Arisaema
triphyllum (Araceae). American Journal of Botany, 90(12), 1729-1735.
Warren, R.J.II., Labatore, A., & Candeias, M. (2017). Allelopathic invasive tree (Rhamnus cathartica) alters native plant communities. Plant Ecology, 218(10), 1233-1241.
Weryszko-Chmielwska, E., Piotrowska-Weryszko, K., & Dabrowska, A. (2019). Response of Tilia sp. L. to climate warming in urban conditions-
phenological and aerobiological studies. Urban Forestry & Urban Greening, 43, 126369.
Whitney, G.G. (1986). A demographic analysis of Rubus idaeus and Rubus pubescens. Canadian Journal of Botany, 64, 2916-2921.
Zouhar, Kris. (2011). Rhamnus cathartica, R. davurica. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/shrub/rhaspp/all.html [2019, November 5].
Top Related