Use Of Species Distribution Models Research Articles

PPDC: An online platform for the prediction of plant distributions in China

Abstract The survival and reproduction of plants in a particular region are closely related to the local ecological niche. The use of species distribution models based on the ecological niche concept to predict potential distributions can effectively guide the protection of endangered plants, prevention and control of invasive plants, and plant introduction and ex-situ conservation. However, traditional methods and processes for predicting potential distributions of plants are tedious and complex, requiring the collection and processing of large amounts of data and the manual operation of multiple tools. Therefore, it is difficult to achieve large-scale prediction of the potential distributions of plants. To address these limitations, by collecting and organizing a large amount of basic data, occurrence records, and environmental data and integrating species distribution models and mapping techniques, a workflow to automatically predict the potential distributions of Chinese plants was established, thus the innovative work of predicting the potential distributions of 32,000 species of plants in China was completed. Furthermore, an online platform for predicting plant distributions in China (PPDC) based on visualization technology was developed, providing a basis for sharing the prediction results across a wide range of scientists and technologists. Users can quickly access information about the potential distributions of plants in China, providing a reference for the collection, preservation, and protection of plant resources. In addition, users can quickly predict the potential distribution of a certain plant in a certain region in China according to specific needs, thus providing technical support for research and a basis for biodiversity conservation.

Mapping human‐carnivore coexistence: approaches to integrating anthropogenic influences on carnivore distribution and connectivity modelling

AbstractInterdisciplinary approaches to modelling human‐wildlife coexistence have gained much attention in recent years, especially regarding carnivores which are subject to historical human persecution and dynamic attitudes and cultural values. Accordingly, there have been many attempts to gain insights into anthropogenic influences on carnivores through the use of species distribution models and connectivity analyses. However, there have been no global reviews on how anthropogenic influences are incorporated into such models. We conducted a literature review of global terrestrial carnivore species distribution and connectivity studies from 1995 to 2021 to evaluate the type of measure, frequency of use and spatial scale of the predictor variables used to describe environmental conditions and assess anthropogenic influences in such predictive models. We evaluated 2495 variables from 263 carnivore distribution and connectivity studies and found that variables used to describe environmental conditions are four times more commonly used than those used to assess anthropogenic influences on carnivores. However, the number of anthropogenic covariates used in carnivore distribution and connectivity studies has increased by 4.7% per year, and the spatial resolution of those covariates has simultaneously decreased by 7.6% per year. We observed that anthropogenic covariates were used more in studies occurring in the global north and for larger‐bodied carnivores (>15 kg), suggesting the need for better integration of anthropogenic metrics into predictive models more widely and for smaller‐bodied carnivores. To help guide and advance the further use of sociological data into predictive spatial models for carnivores, we propose a framework that emphasizes integration of sociological methodologies and data sources ranging from exploratory expert elicitation to targeted focus group interviews at multiple phases in the conservation planning process.

Some bats are here: Reducing the Wallacean shortfall of bats in the amazon.

The Amazon rainforest has approximately 23% of its sampled area dedicated to bats, making it one of the least sampled and most diverse regions for bats in Brazil. The lack of sampling results in a lack of knowledge regarding the accurate geographical distribution of bat species. This lack is referred to as the Wallacean shortfall, which should be addressed with primary data obtained from insitu collections. However, the use of Species Distribution Models (SDMs) can help alleviate this gap. The states of Pará and Acre are located in the Brazilian Amazon. So, our objective is to decrease the Wallacean shortfall concerning Amazonian bat species. To achieve this, we provide (i) a list of bat species sampled in the states of Pará and Acre in the last 5 years (2017 to 2022); (ii) the potential distribution of species considered as new occurrences for the region; and (iii) the potential distribution of species classified as Data Deficient (DD) and Near Threatened (NT) according to the IUCN classification. With 96 nights of collection and 129,600 m2h of mist netting, we obtained 75 bat species, with an estimated total of 94.78 species. Additionally, 21 species were considered as range extensions. The Brazilian Amazon region has a vast geographic expanse and few established research centers, resulting in a limited sampling of bats and other biological groups. Furthermore, we draw attention to the significant number of bat species with expanded geographical distributions, with 21 out of the 75 sampled species. This should be a reminder that primary biogeographic data is still necessary for the neotropical region.

The effectiveness of species distribution models in predicting local abundance depends on model grain size.

The use of species distribution models (SDMs) to predict local abundance has been often proposed and contested. We tested whether SDMs at different spatiotemporal resolutions may predict the local density of 14 bird species of open/semi-open habitats. SDMs were built at 1 ha and 1 km, and with long-term versus a mix of current and long-term climatic variables. The estimated environmental suitability was used to predict local abundance obtained by means of 275 linear transects. We tested SDM ability to predict abundance for all sampled sites versus occurrence sites, using N-mixture models to account for imperfect detection. Then, we related the R2 of N-mixture models to SDM traits. Fine-grain SDMs appeared generally more robust than large-grain ones. Considering the all-transects models, for all species environmental suitability displayed a positive and highly significant effect at all the four combinations of spatial and temporal grains. When focusing only on occurrence transects, at the 1 km grain only one species showed a significant and positive effect. At the 1 ha grain, 62% of species models showed (over both climatic sets) a significant or nearly significant positive effect of environmental suitability on abundance. Grain was the only factor significantly affecting the model's explanatory power: 1 km grain led to lower amounts of variation explained by models. Our work re-opens the debate about predicting abundance using SDM-derived suitability, emphasizing the importance of grains and of spatiotemporal resolution more in general. The incorporation of local variables into SDMs at fine grains is key to predict local abundance. SDMs worked out at really fine grains, approaching the average size of territory or home range of target species, are needed to predict local abundance effectively. This may result from the fact that each single cell may represent a potential territory/home range, and hence a higher suitability over a given area means that more potential territories occur there.

Effect of straying, reproductive strategies, and ocean distribution on the structure of American shad populations

AbstractThe use of species distribution models has proliferated, providing insights for sustainable management of migratory species in a globally changing environment. However, many of these models are based on statistical relationships developed from historical conditions that may not perform well under changing or even analogous conditions caused by climate change. In this paper, we used a mechanistic species distribution model called GR3D (Global Repositioning Dynamics for Diadromous Fish Distribution) to examine the integrated dynamics of American shad (Alosa sapidissima) populations across their native range along the Eastern U.S. coast, where the species demonstrates latitudinal variations in life histories and reproductive strategies. The initial design of the model was adapted to incorporate region‐specific parameterization to fit the species ecology. Then, a sensitivity analysis was performed to test the influences of uncertain processes regarding American shad distribution at sea, straying and reproduction on key characteristics of the species distribution. The sensitivity analysis showed the influence of the Allee effect (i.e., “depensatory” process) and the homing rate (i.e., fidelity to the breeding sites) on the probability of presence and abundances among catchments and metapopulations estimated by the model. Contrary to the homing rate, the distance of straying did not change the estimated number of metapopulations or abundances. Homing strength, however, was quite influential. The integration of complex migration patterns during the marine phase (i.e., wintering and summering offshore areas) provided more likely estimates of the species' overall distribution. Overall, our study illustrated the utility of incorporating factors governing the large‐scale distribution of migratory species to improve local management.

Multitemporal Land Use and Cover Analysis Coupled with Climatic Change Scenarios to Protect the Endangered Taxon Asphodelus bento-rainhae subsp. bento-rainhae.

Climate change and land use and land cover (LULC) change are impacting the species' geographic distribution, causing range shifts and reducing suitable habitats. Asphodelus bento-rainhae subsp. bento-rainhae (AbR) is an endangered endemic plant restricted to Serra da Gardunha (Portugal), and knowledge of those changes will help to design conservation measures. MaxEnt was used to model AbR's current distribution and project it into the future, 2050, using the Shared Socioeconomic Pathway SSP3-7. The Portuguese LULC maps from 1951-1980, 1995, 2007, and 2018 were used to assess and quantify LULC changes over time. The results showed that the AbR current predicted distribution matches its actual known distribution, which will not be affected by future predicted climate change. The significant LULC changes were observed during the study periods 1951-1980 to 2018, particularly between 1951-1980 and 1995. Scrubland and Agriculture decreased by 5% and 2.5%, respectively, and Forests increased by 4% in the study area. In the occurrence area, Agriculture increased, and Forests decreased between 1980 and 2018, due to Orchard expansion (34%) and declines in Chestnut (16.9%) and Pine (11%) areas, respectively. The use of species distribution models and the LULC change analysis contributed to understanding current and future species distribution. The LULC changes will have a significant impact on future species distribution. To prevent the extinction of this endemic species in the future, it is crucial to implement conservation measures, namely species monitoring, replantation, and germplasm conservation, in addition to guidelines for habitat conservation.

The influence of environmental factors on the historical distribution of Biomphalaria pfeifferi in the Tshwane Metropolitan Municipality

Abstract. Schistosomiasis remains the most endemic disease in tropical regions. Transmission rates and prevalence of schistosomiasis in Africa are relatively high compared to other regions. This is mostly associated with the lack of clean water and sanitation as necessities to curbing the spread of the disease. Over 90% of people in sub-Saharan Africa (SSA) are deprived of clean water and sanitation. 60-80% of the children in South African communities are infected. Climate variability plays a role in the distribution of vector-borne diseases by affecting intermediate host reproduction and changing their habitats thereby decreasing or increasing host survival and disease transmission ability. This study aims to determine the influence of environmental factors on the spatial distribution of Biomphalaria pfeifferi in the Tshwane Metropolitan Municipality. The use of species distribution models (SDMs) to model the intermediate snail hosts habitats in South Africa is uncommon, compared to other African countries. To our knowledge no modelling study has been done in South Africa particularly in the Tshwane Municipality, and this has therefore, paved a way for this study. A systematic literature review was conducted to identify all the important climatic variables that impact on the distribution of bilharzia-transmitting snails. Biomphalaria pfeifferi occurrence data was accessed from the National Freshwater Snails Collection, and the climate data was extracted from the ERA5 website respectively. The principal component analysis was used to reduce the multidimensionality of the dataset, with this framework tool, Pearson's correlations helped identify the variables showing a high correlation with the snail datasets and these were included in the ecological niche model. The following climatic variables: temperature, rainfall, water temperature, and other climate-related phenomena such as droughts and flooding influence the distribution patterns of intermediate snail hosts. The model showed high species presence along the central parts of Tshwane, mostly in the following areas Roodeplaat, Mamelodi, Winterveldt, and Refilwe. However, the south-eastern parts of the municipality did not indicate any species occurrence. The findings confirm that environmental factors impact on the historical distribution of Biomphalaria pfeifferi in the Tshwane Metropolitan Municipality. The response curves from the MaxEnt model depict the relationship between habitat suitability and environmental factors.

Use of species distribution models to study habitat suitability for sustainable management and conservation in the Indian subcontinent: A decade's retrospective

Loss of native habitat has been identified as one of the crucial drivers of the growing biodiversity crisis, acting toward the deleterious effects on individual fitness, functional diversity, changes in species richness and community structure. The objective of the study was to review the species distribution modeling works done in the Indian subcontinent throughout the last decade (2012–2021) to highlight the urgent issues of biodiversity conservation and sustainable management. Species distribution model (SDM), ecological niche model (ENM), and habitat suitability model (HSM) are, in general, used to predict the niche of a species. The Scopus search engine result was used for conducting the literature search of peer-reviewed articles. Topics examined included estimation of species' potential distribution area, spread of introduced species, deadly diseases or pests, habitat loss or future range shift of species due to anthropogenic alterations and climate change. The analysis showed that the majority of the works were focused on predicting native species distribution patterns (n= 119, 82%) rather than the invasion of alien species in the native ecosystem (n= 26, 18%) indicating the further scope of research in these areas. The scientific articles highlighted that the modeling results could be utilized efficiently to design conservation areas if not biased toward charismatic species. A coordinating institute at the inter-ministerial level can help to undertake policy-related planning for sustainable management and execution of decisions.

Integrating Different Scales into Species Distribution Models: A Case for Evaluating the Risk of Plant Invasion in Chinese Protected Areas under Climate Change

Species distribution models (SDMs) based on fine-scale environmental data may reduce the uncertainty in predicting species distributions. However, many scientists have also projected the robust potential distributions of species using environmental data of different scales and found that the potential distributions modeled using SDMs are scale dependent. This may be due to the impact of the scale effect on species richness (as well as on multi-species distributions). To eliminate the impact of the scale effect, we aim to develop an improved method to integrate different scales into species distribution models. We use protected areas as the study regions and propose the hypothesis that there is a spatial element to the threat of invasive species for protected areas under climate change. We use Maxent to compute the current and future invasion ability and invasion inequality of invasive species for protected areas based on the potential distributions of species across different scales to evaluate the risk of invasive species. We find that an increase in the number of present records could reduce the accuracy of SDMs. There is a significant linear relationship between the fine-scale and coarse-scale risk of invasive species of alien plants in protected areas, and an appropriate scale should thus be selected to assess species risk based on this linear relationship of invasive risk. There is a significant relationship between the potential of IAPS to invade protected areas and the invasion inequality of IAPS in protected areas across all scales, and 5.0 arcminutes is the most appreciate scale to evaluate the risk of IAPS for protected areas under climate change based on principal component analysis. We provide new insights into the use of species distribution models coupled with different spatial scales to analyze the regional risks associated with species and to assess regional biodiversity.

Developing species distribution models for critically endangered species using participatory data: The European sturgeon marine habitat suitability

Increasing knowledge on distribution and habitat selection of coastal fish species is essential for the management of marine environments, and can be provided through the use of Species Distribution Models (SDM). Opportunistic and participatory data, such as recreational or commercial bycatch data, can provide precious information at large scale and low cost to estimate fish spatial distributions, but are subject to biases (e.g. lack of absence data, non-random sampling scheme). The European sturgeon (Acipenser sturio) is a critically endangered diadromous fish and its marine habitat preferences are unknown. The goal of this study was to investigate key variables driving A. sturio distribution and predict its current distribution in inshore waters along the European Atlantic coast. We developed two correlative SDM in A. sturio marine habitats based on incidental observations (2012–2021) and two different sets of environmental predictors. In both attempts, six methods which differ according to the biases they aim to reduce (i.e. oversampled areas or environmental conditions, dependence between calibration and validation datasets) and the input dataset used (i.e. random pseudo-absences or target-group pseudo-absences) were applied at various stages of the model building and evaluation process. By comparing potentially biased and bias-corrected predictions, we found similar satisfactory level of evaluation and spatial patterns of habitat suitability. Marine areas predicted as the most suitable were distributed along the French Atlantic coast, the Seine and Somme bays, and at a lesser extent around the Rhine delta and in some coastal areas of the Cantabrian Sea (Northern Spain). We found a strong influence of the bathymetry, temperature, and salinity as well as an influence of the bottom current velocity and of the distance to the source population in explaining A. sturio marine distribution, while the nature of the substrate was not retained by the models. Results of this study could pave the way toward more integrated conservation measures for this critically endangered fish.

Species Distribution Models at Regional Scale: Cymodocea nodosa Seagrasses

Despite their ecological and socio-economic importance, seagrasses are often overlooked in comparison with terrestrial ecosystems. In the Canarian archipelago (Spain), Cymodocea nodosa is the best-established species, sustaining the most important marine ecosystem and providing ecosystem services (ES) of great relevance. Nevertheless, we lack accurate and standardized information regarding the distribution of this species and its ES supply. As a first step, the use of species distribution models is proposed. Various machine learning algorithms and ensemble model techniques were considered along with freely available remote sensing data to assess Cymodocea nodosa’s potential distribution. In a second step, we used InVEST software to estimate the ES provision by this phanerogam on a regional scale, providing spatially explicit monetary assessments and a habitat degradation characterization due to human impacts. The distribution models presented great predictive capabilities and statistical significance, while the ES estimations were in concordance with previous studies. The proposed methodology is presented as a useful tool for environmental management of important communities sensitive to human activities, such as C. nodosa meadows.

Alpine marmot (Marmota marmota) distribution evolution under climate change: The use of species distribution models at a local scale in the western Pyrenees massif (France)

The Alpine marmot (Marmota marmota) is a social mammal living in mountainous grassland areas and has the particularity to hibernate in winter. Recent studies on a population in the French Alps found that climate change is affecting Alpine marmot population dynamics and might impact their overall distribution in the future. Using Species Distribution Models (SDMs), the effect of climate change on Alpine marmot's future distribution was investigated at a local scale, in the western part of the Pyrenean massif (New-Aquitaine region, France). This scale was chosen as an appropriate action scale for the conservation strategy for the species. Three climatic scenarios were used (RCP 2.6, RCP 4.5, and RCP 8.5) over three future 30-year periods (2021–2050, 2040–2070, 2071–2100) to predict the short- to long-term potential distribution of the target species. The results are consistent with naturalistic knowledge of the species´ ecological needs in terms of variable importance and response type. Mean maximum temperature in winter, standard-deviation of daily temperature in winter, along with the median rainfall amount in summer were the three most important climatic variables. Predictions under the two most pessimistic climate scenarios showed potential large habitat loss. In the long term, for RCP 4.5, an estimated habitat loss of 18% was predicted. In the case of RCP 8.5, a higher impact was predicted, with a 54% habitat loss. Our results show that high impact due to climate change can be expected at a long term. In addition, if winter climatic conditions are important for marmot survival through hibernation, drought in summer might be one of the drivers of future population dynamic and distribution. Our findings can be applied for other species living in grassland mountainous environments and for which access to food resources in summer is essential, facilitating the conservation of target areas.

Modeling of Valeriana wallichii Habitat Suitability and Niche Dynamics in the Himalayan Region under Anticipated Climate Change.

Simple SummaryIn the modern era of the anthropocene, an increase in the accumulation of greenhouse gases in the atmosphere has resulted in the global rise of the Earth’s temperature. This has resulted in the habitat shift of various plant and animal species. Plant species that are endemic show a narrow distribution range and inhabit higher elevations are highly susceptible to the impacts of global warming. The present study discusses the habitat distribution of Valeriana wallichi, an endangered medicinal herb. We also predicted the impact of climate change on its distribution range and niche dynamics. Results reveal significant contraction in the possible habitats of this herb under future climatic scenarios, with RCP 8.5 for 2070 showing the highest habitat loss. Niche equivalency and similarity tests describe that the habitats could be compared but not identical between current and future climatic scenarios. From the current study, we conclude that the habitats of Valeriana wallichii are highly vulnerable to climate shifts. This study can not only be used to alleviate the threat to this plant by documenting the unexplored populations, restoring the degraded habitats through rewilding and launching species recovery plans in the natural habitats but could also be used by various conservation biologists to recover the declining status of various highly valued species across the globe.An increase in atmospheric greenhouse gases necessitates the use of species distribution models (SDMs) in modeling suitable habitats and projecting the impact of climate change on the future range shifts of the species. The present study is based on the BIOMOD ensemble approach to map the currently suitable habitats and predict the impact of climate change on the niche shift of Valeriana wallichii. We also studied its niche dynamics using the ecospat package in R software. Values of the area under curve (AUC) and true skill statistics (TSS) were highly significant (>0.9), which shows that the model has run better. From 19 different bioclimatic variables, only 8 were retained after correlation, among which bio_17 (precipitation of driest quarter), bio_1 (annual mean temperature), and bio_12 (annual mean precipitation) received the highest gain. Under future climate change, the suitable habitats will be significantly contracted by −94% (under representative concentration pathway RCP 8.5 for 2070) and −80.22% (under RCP 8.5 for 2050). There is a slight increase in habitat suitability by +16.69% (RCP 4.5 for 2050) and +8.9% (RCP 8.5 for 2050) under future climate change scenarios. The equivalency and similarity tests of niche dynamics show that the habitat suitability for current and future climatic scenarios is comparable but not identical. Principal Component Analysis (PCA) analysis shows that climatic conditions will be severely affected between current and future scenarios. From this study, we conclude that the habitats of Valeriana wallichii are highly vulnerable to climate shifts. This study can be used to alleviate the threat to this plant by documenting the unexplored populations, restoring the degraded habitats through rewilding, and launching species recovery plans in the natural habitats.

The shadow model: how and why small choices in spatially explicit species distribution models affect predictions.

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence of distributional shifts of terrestrial and aquatic populations. These models permit, for example, the quantification of range shifts, the estimation of species co-occurrence, and the association of habitat to species distribution and abundance. The increasing complexity of contemporary SDMs presents new challenges—as the choices among modeling options increase, it is essential to understand how these choices affect model outcomes. Using a combination of original analysis and literature review, we synthesize the effects of three common model choices in semi-parametric predictive process species distribution modeling: model structure, spatial extent of the data, and spatial scale of predictions. To illustrate the effects of these choices, we develop a case study centered around sablefish (Anoplopoma fimbria) distribution on the west coast of the USA. The three modeling choices represent decisions necessary in virtually all ecological applications of these methods, and are important because the consequences of these choices impact derived quantities of interest (e.g., estimates of population size and their management implications). Truncating the spatial extent of data near the observed range edge, or using a model that is misspecified in terms of covariates and spatial and spatiotemporal fields, led to bias in population biomass trends and mean distribution compared to estimates from models using the full dataset and appropriate model structure. In some cases, these suboptimal modeling decisions may be unavoidable, but understanding the tradeoffs of these choices and impacts on predictions is critical. We illustrate how seemingly small model choices, often made out of necessity or simplicity, can affect scientific advice informing management decisions—potentially leading to erroneous conclusions about changes in abundance or distribution and the precision of such estimates. For example, we show how incorrect decisions could cause overestimation of abundance, which could result in management advice resulting in overfishing. Based on these findings and literature gaps, we outline important frontiers in SDM development.

Multi-taxon biodiversity assessment of Southern Patagonia: Supporting conservation strategies at different landscapes

In the last years, different spatial analyses were developed to support multi-taxon biodiversity conservation strategies. In fact, the use of species distribution models as input allowed to create spatial decision-support maps. Of special interest are maps of potential biodiversity (MPB), which define distribution and ecological requirements of relevant species and maps of priority conservation areas (MPCA), which define priority areas considering endemism and richness. The objective of this paper was to assess multi-taxon biodiversity based on two different spatial analyses and to test their efficiency to support conservation decision at Patagonia. We computed 119 potential habitat suitability maps (one deer, birds, lizards, darkling-beetles, plants) with ENFA (Environmental Niche Factor Analysis) and 15 environmental variables, using Biomapper software. ENFA calculate two ecological indexes (marginality and specialization) which describe the narrowness of species niches and how extreme are the optimum environmental conditions related to the whole study area. These maps were combined obtaining a MPB and MPCA using Zonation software. Multivariate analyses were performed to compare methodologies, analysing environmental variables, ecological areas, forest types and protected areas. Multivariate and ecological indexes showed that deer, lizards and darkling-beetles presented a narrow range, while birds and plants presented a large range of marginality and specialization mainly related to vegetation and climate. At provincial level, highest potential biodiversity and conservation priority values were related to shrublands and humid steppes. However, MPCA showed higher values related to forests and alpine vegetation due to endemism, while MPB showed differences among forest types. These analyses showed that the most valuable areas were not represented in the protected areas, however, many higher conservation priority values were found inside the protected compared with unprotected areas. Different spatial decision-support maps presented similar outputs at provincial scale, but differed in the forest landscape matrix. Both methodologies can be used to plan conservation strategies depending on the specific objectives (e.g. highlighting richness or endemism).

Contributions of Distribution Modelling to the Ecological Study of Psittaciformes

We provide an overview of the use of species distribution modeling to address research questions related to parrot ecology and conservation at a global scale. We conducted a literature search and applied filters to select the 82 most relevant studies to discuss. The study of parrot species distribution has increased steadily in the past 30 years, with methods and computing development maturing and facilitating their application for a wide range of research and applied questions. Conservation topics was the most popular topic (37%), followed by ecology (34%) and invasion ecology (20%). The role of abiotic factors explaining parrot distribution is the most frequent ecological application. The high prevalence of studies supporting on-ground conservation problems is a remarkable example of reduction in the research–action gap. Prediction of invasion risk and assessment of invasion effect were more prevalent than examples evaluating the environmental or economic impact of these invasions. The integration of species distribution models with other tools in the decision-making process and other data (e.g., landscape metrics, genetic, behavior) could even further expand the range of applications and provide a more nuanced understanding of how parrot species are responding to their even more changing landscape and threats.

Modelling temperature-driven changes in species associations across freshwater communities.

Due to global climate change-induced shifts in species distributions, estimating changes in community composition through the use of Species Distribution Models has become a key management tool. Being able to determine how species associations change along environmental gradients is likely to be pivotal in exploring the magnitude of future changes in species' distributions. This is particularly important in connectivity-limited ecosystems, such as freshwater ecosystems, where increased human translocation is creating species associations over previously unseen environmental gradients. Here, we use a large-scale presence-absence dataset of freshwater fish from lakes across the Fennoscandian region in a Joint Species Distribution Model, to measure the effect of temperature on species associations. We identified a trend of negative associations between species tolerant of cold waters and those tolerant of warmer waters, as well as positive associations between several more warm-tolerant species, with these associations often shifting depending on local temperatures. Our results confirm that freshwater ecosystems can expect to see a large-scale shift towards communities dominated by more warm-tolerant species. While there remains much work to be done to predict exactly where and when local extinctions may take place, the model implemented provides a starting-point for the exploration of climate-driven community trends. This approach is especially informative in regards to determining which species associations are most central in shaping future community composition, and which areas are most vulnerable to local extinctions.

Novel Use of Species Distribution Modeling to Identify High Priority Sites for American Woodcock Habitat Management

Most species distribution models (SDMs) predict the probability of presence (POP) of a species at any location based on an analysis of known occurrences and environmental variables; however, such SDMs cannot predict how much the POP of a species that requires young forest would change if a new patch of young forest is created at a certain location. We developed a new SDM tool to identify and prioritize sites where forest management efforts to create young forest vegetation would likely have the most positive effect for Scolopax minor (American Woodcock), an important umbrella species for other birds that require similar habitat. The primary output of the tool was a 50-m raster of the predicted POP in any upland forest location in the state after young forest is created. We conducted a preliminary assessment of the predictions of the tool, and concluded that our new tool can allow land managers to identify optimal locations on their properties to create young forest for woodcock, and help conservation agencies identify private landowners with suitable properties for improving woodcock habitat.

The evolutionary genomics of species' responses to climate change.

Climate change is a threat to biodiversity. One way that this threat manifests is through pronounced shifts in the geographical range of species over time. To predict these shifts, researchers have primarily used species distribution models. However, these models are based on assumptions of niche conservatism and do not consider evolutionary processes, potentially limiting their accuracy and value. To incorporate evolution into the prediction of species' responses to climate change, researchers have turned to landscape genomic data and examined information about local genetic adaptation using climate models. Although this is an important advancement, this approach currently does not include other evolutionary processes-such as gene flow, population dispersal and genomic load-that are critical for predicting the fate of species across the landscape. Here, we briefly review the current practices for the use of species distribution models and for incorporating local adaptation. We next discuss the rationale and theory for considering additional processes, reviewing how they can be incorporated into studies of species' responses to climate change. We summarize with a conceptual framework of how manifold layers of information can be combined to predict the potential response of specific populations to climate change. We illustrate all of the topics using an exemplar dataset and provide the source code as potential tutorials. This Perspective is intended to be a step towards a more comprehensive integration of population genomics with climate change science.

Climatic niche shifts in 815 introduced plant species affect their predicted distributions

AbstractAimIntroduced species often occupy different climates in their introduced than their native range, but to what degree do such ‘climatic niche shifts’ interfere with our ability to predict invasions? Answering this question is crucial if we are to understand the threat invasive species pose to human and natural systems, especially given the ever increasing use of species distribution models as tools for invasive species risk assessment and management. Here we investigated the degree to which climatic niche shifts interfered with the transferability of native‐ and introduced‐range species distribution models.LocationOur dataset consisted of c. 14 million occurrences distributed worldwide.Time periodOccurrence data were collected from online repositories dating from c. 1600 with the vast majority being from the 20th century. Climatic data represent means between 1970 and 2000.Major taxa studiedOur database represented 815 terrestrial plant species.MethodsWe used ordination to identify climatic niche shifts as species moved between continents. Next, we trained separate Maxent models using native‐ or introduced‐range occurrences, and projected those models into each species’ introduced range. We compared the ordination and Maxent models to determine whether niche shifts were associated with errors in Maxent predictions.ResultsModels trained on native‐range occurrences poorly predicted introduced‐range occurrences, and transferability was lowest in species with large climatic niche shifts. Directional shifts in species’ predicted geographic distributions mirrored their niche dynamics. This is concerning because native‐range data are often used to predict introduced‐range distributions.Main conclusionsOur results highlight the importance of considering niche shifts when modelling the potential geographic distributions of introduced species, and cast doubt on the assumption that the climatic niche of a species can be transferred between native and invasive ranges.