Species Distribution Models Projections Research Articles

Connecting population functionality with distribution model predictions to support freshwater and marine management of diadromous fish species

Diadromous fish species have a complex life cycle during which they migrate between marine and freshwater habitats. They experience multiple human-induced pressures in both environments, likely exacerbated by climate change, leading to dramatic population declines across their distribution ranges. Currently Species Distribution Models (SDMs) have been applied separately in both their continental and marine habitats to improve our understanding of their lifecycles and help with species management. Integrating the freshwater-sea continuum into the decisions would now be a step further in improving their management. With this objective, we developed a decision tree that links marine and freshwater SDM outputs with current observations of population functionality and suggested management guidance options for the viability of these species. Potential effects of climate change were included through future SDM projections to guide integrative and long-term management. Several criteria were proposed to assess the SDM validity considering the main sources of SDM uncertainties and local expert knowledge on habitat and population status. The framework was applied to approximately one hundred catchments from southern Portugal to southern Scandinavia for four diadromous species. At the European level, management guidance options differed between the two anadromous and two catadromous species. Platichthys flesus and Chelon ramada European populations seemed in better state than those of Alosa alosa and A. fallax. Finally, with the help of national diadromous species experts, we focused on four catchments distributed along the European latitudinal gradient to test the proposed methodology and demonstrate local management challenges in terms of freshwater-sea continuity.

Addressing uncertainty when projecting marine species' distributions under climate change

Species distribution models (SDMs) have been widely used to project terrestrial species' responses to climate change and are increasingly being used for similar objectives in the marine realm. These projections are critically needed to develop strategies for resource management and the conservation of marine ecosystems. SDMs are a powerful and necessary tool; however, they are subject to many sources of uncertainty, both quantifiable and unquantifiable. To ensure that SDM projections are informative for management and conservation decisions, sources of uncertainty must be considered and properly addressed. Here we provide ten overarching guidelines that will aid researchers to identify, minimize, and account for uncertainty through the entire model development process, from the formation of a study question to the presentation of results. These guidelines focus on correlative models and were developed at an international workshop attended by over 50 researchers and practitioners. Although our guidelines are broadly applicable across biological realms, we provide particular focus to the challenges and uncertainties associated with projecting the impacts of climate change on marine species and ecosystems.

The invasive alga Gracilaria vermiculophylla in the native northwest Pacific under ocean warming: Southern genetic consequence and northern range expansion

Ocean warming is one of the most important factors in shaping the spatial distribution and genetic biodiversity of marine organisms worldwide. The northwest Pacific has been broadly illustrated as an essential seaweed diversity hotspot. However, few studies have yet investigated in this region on whether and how past and ongoing climate warming impacted the distribution and genetic pools of coastal seaweeds. Here, we chose the invasive species Gracilaria vermiculophylla as a model, and identified multiple genetic lineages in the native range through genome-scale microsatellite genotyping. Subsequently, by reconstructing decadal trends of sea surface temperature (SST) change between 1978 and 2018, we found that SST in northern Japan and the East China Sea indeed increased broadly by 0.25-0.4°C/decade. The projections of species distribution models (SDMs) under different future climate change scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) indicated that a unique genetic pool of G. vermiculophylla at its current southern range limit (i.e. the South China Sea) is at high risk of disappearance, and that the populations at its current northern range limit (i.e. in Hokkaido region) will undergo poleward expansions, particularly by the year 2100. Such responses, along with this species’ limited dispersal potential, may considerably alter the contemporary distribution and genetic composition of G. vermiculophylla in the northwest Pacific, and ultimately threaten ecological services provided by this habitat-forming species and other associated functional roles.

Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models

Species distribution models (SDMs) are subject to many sources of uncertainty, limiting their application in research and practice. One of their main limitations is the quality of the distributional data used to calibrate them, which directly influences the accuracy of model predictions. We propose a standardized methodology to create maps, describing the limitations of occurrence data for covering the distribution of a species. We develop a set of tools based on the general framework of Maps of Biogeographical Ignorance to describe the main sources of data‐driven uncertainty: taxonomic stability, environmental similarity, geographical proximity and temporal decay of the underlying biodiversity data. The so‐derived indicators of data‐driven uncertainty account for inventory completeness, taxonomic quality, time since the surveys and geographical (and environmental) distance to localities with information. These indicators form the basis of ignorance maps, which can be used to visualize the reliability of SDM projections in geographical space, to estimate the uncertainty of these predictions and to identify target survey areas. To demonstrate the application of our approach, we use data on fourteen Iberian species of Scarabaeidae dung beetles. Data‐driven uncertainty is widespread even for this well‐surveyed group; more than 60% of the region has distributional uncertainty values higher than 0.6, and 30% higher than 0.7. Ignorance maps can be jointly evaluated with SDM predictions to generate spatially explicit maps of uncertainty, identifying where predictions are reliable/unreliable. Neglecting such uncertainty can severely affect SDM effectiveness, as it can introduce biases and inaccuracies into the measured species–environment relationships. These errors could result in incorrect theoretical or practical applications, including ill‐advised conservation actions. We therefore advocate for the routine use of ignorance maps or similar techniques as supporting information in SDM applications.

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution.

Many species distribution models (SDMs) are built with precise but geographically restricted presence-absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species' niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

Potential alien ranges of European plants will shrink in the future, but less so for already naturalized than for not yet naturalized species

AbstractAimsThe rapid increase in the number of species that have naturalized beyond their native range is among the most apparent features of the Anthropocene. How alien species will respond to other processes of future global changes is an emerging concern and remains poorly misunderstood. We therefore ask whether naturalized species will respond to climate and land use change differently than those species not yet naturalized anywhere in the world.LocationGlobal.MethodsWe investigated future changes in the potential alien range of vascular plant species endemic to Europe that are either naturalized (n = 272) or not yet naturalized (1,213) outside of Europe. Potential ranges were estimated based on projections of species distribution models using 20 future climate‐change scenarios. We mapped current and future global centres of naturalization risk. We also analysed expected changes in latitudinal, elevational and areal extent of species’ potential alien ranges.ResultsWe showed a large potential for more worldwide naturalizations of European plants currently and in the future. The centres of naturalization risk for naturalized and non‐naturalized plants largely overlapped, and their location did not change much under projected future climates. Nevertheless, naturalized plants had their potential range shifting poleward over larger distances, whereas the non‐naturalized ones had their potential elevational ranges shifting further upslope under the most severe climate change scenarios. As a result, climate and land use changes are predicted to shrink the potential alien range of European plants, but less so for already naturalized than for non‐naturalized species.Main conclusionsWhile currently non‐naturalized plants originate frequently from mountain ranges or boreal and Mediterranean biomes in Europe, the naturalized ones usually occur at low elevations, close to human centres of activities. As the latter are expected to increase worldwide, this could explain why the potential alien range of already naturalized plants will shrink less.

Quaternary climatic fluctuations influence the demographic history of two species of sky-island endemic amphibians in the Neotropics

We evaluated the role of Quaternary climatic fluctuations on the demographic history and population structure of amphibian species endemic to the ‘campo rupestre’ in the Neotropics, evaluating their distributional shifts, demographic changes, and lineage formation from the end of Pleistocene to present. We chose two anurans endemic to the high-elevation ‘campo rupestre’ in the Espinhaço Range (ER) in northeastern and southeastern Brazil (Bokermannohyla alvarengai and Bokermannohyla oxente), as models to test the role of Quaternary climatic fluctuations over their distribution range in this region. We collected tissue samples throughout their distribution range and used statistical phylogeography to examine processes of divergence and population demography. We generated spatial–temporal reconstructions using Bayesian inference in a coalescent framework in combination with hind-cast projections of species distribution models (SDMs). We also used the results and literature information to test alternative diversification scenarios via approximate Bayesian computation (ABC). Our results show that Quaternary climatic fluctuations influenced the geographic ranges of both species showing population expansion during the last glacial maximum (LGM) and range contraction during interglacial periods, as inferred from selected ABC models and from past projections of SDMs. We recovered Pleistocene diversification for both species occuring in distinctly unique periods for each taxon. An older and range-restricted lineage was recovered in a geographically isolated geological massif, deserving conservation and further taxonomic study. The diversification and distribution of these amphibian species endemic to the Neotropical ‘campo rupestre’ were influenced by Quaternary climatic fluctuations. The expansion of cold adapted species restricted to higher elevations during glacial periods and their concomitant retraction during interglacial periods may have been crucial for producing patterns of species richness and endemism along elevation gradients in tropical and subtropical domains. Such processes may influence the evolution of the biota distributed in heterogeneous landscapes with varied topography.

Assessing the reliability of species distribution projections in climate change research

AbstractAimForecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species’ distribution response to changing climate. In this study, we provide an overview of common modelling practices in the field and assess the reliability of model predictions using a virtual species approach.LocationGlobal.MethodsWe first review papers published between 2015 and 2019. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and random forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions.ResultsMost SDM papers relied on single models (65%) and small samples (N < 50, 62%), used presence‐only data (85%), binarized models' output (74%) and used a split‐sample validation (94%). Our simulation reveals that the split‐sample validation tends to be over‐optimistic compared to the real performance, whereas spatial block validation provides a more honest estimate, except when datasets are environmentally biased. The binarization of predicted probabilities of presence reduces models’ predictive ability considerably. Sample size is one of the main predictors of the real model accuracy, but has little influence on estimated accuracy. Finally, the inclusion of ecologically irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion.Main conclusionsOur ability to predict future species distribution is low on average, particularly when models’ predictions are binarized. A robust validation by spatially independent samples is required, but does not rule out inflation of model accuracy by assumption violation. Our findings call for caution in the application and interpretation of SDM projections under different climates.

Genetic structure and life history are key factors in species distribution models of spiny lobsters.

AimWe incorporated genetic structure and life history phase in species distribution models (SDMs) constructed for a widespread spiny lobster, to reveal local adaptations specific to individual subspecies and predict future range shifts under the RCP 8.5 climate change scenario.LocationIndo‐West Pacific.MethodsMaxEnt was used to construct present‐day SDMs for the spiny lobster Panulirus homarus and individually for the three genetically distinct subspecies of which it comprises. SDMs incorporated both sea surface and benthic (seafloor) climate layers to recreate discrete influences of these habitats during the drifting larval and benthic juvenile and adult life history phases. Principle component analysis (PCA) was used to infer environmental variables to which individual subspecies were adapted. SDM projections of present‐day habitat suitability were compared with predictions for the year 2,100, under the RCP 8.5 climate change scenario.ResultsIn the PCA, salinity best explained P. h. megasculptus habitat suitability, compared with current velocity in P. h. rubellus and sea surface temperature in P. h. homarus. Drifting and benthic life history phases were adapted to different combinations of sea surface and benthic environmental variables considered. Highly suitable habitats for benthic phases were spatially enveloped within more extensive sea surface habitats suitable for drifting larvae. SDMs predicted that present‐day highly suitable habitats for P. homarus will decrease by the year 2,100.Main conclusionsIncorporating genetic structure in SDMs showed that individual spiny lobster subspecies had unique adaptations, which could not be resolved in species‐level models. The use of sea surface and benthic climate layers revealed the relative importance of environmental variables during drifting and benthic life history phases. SDMs that included genetic structure and life history were more informative in predictive models of climate change effects.

High thematic resolution land use change models refine biodiversity scenarios: A case study with Belgian bumblebees

AbstractAimProjections of biodiversity scenarios often rely solely on climate change to inform species distribution shifts in the future. Land use projections are rarely used due to their unavailability and, when available, are often at coarse spatial and thematic resolutions, making them unsuitable to capture fine scale habitat suitability. This study aims to (a) show how coupled land use change (LUC) models of high thematic resolution (HTR) can be used in species distribution models (SDM), (b) compare the impacts of HTR and low thematic resolution (LTR) explanatory predictors on biodiversity scenarios and (c) assess the impact of species' present area of occupancy on the effect of thematic resolution in SDMs.LocationBelgiumTaxonBumblebees (Bombus)MethodsWe compared species distribution models with 17 land use predictors (HTR) against models with 5 land use predictors (LTR). We modelled the distribution of 17 bumblebee species in Belgium projected until 2035. We examined how model performance, variable importance and projections of distribution change differed depending on the thematic resolution of the land use predictors.ResultsOverall, HTR models performed better than LTR models. LTR models predicted greater extent per species. HTR projected a greater percentage of range gains, and both models projected similar losses of suitable habitat. However, the percentage loss and connectivity of suitable habitats varied differently for HTR and LTR models along a gradient of rare to common species. The HTR models projected greater loss of suitable areas for rare species and less loss for common species compared to LTR models.Main conclusionsThese results illustrate the importance of using ecologically relevant explanatory variables in SDMs, particularly for rare and localized species with specific habitat requirements. The results also indicate the need for large‐scale LUC projections to improve future biodiversity scenarios under climate change and to improve the ability of conservationists and policymakers to use SDM projections.

Does weighting presence records improve the performance of species distribution models? A test using fish larval stages in the Yangtze Estuary

To obtain realistic forecasts of the impacts of climate change on species habitat suitability, novel approaches based on species distribution models (SDMs) are being developed and scrutinized. We argue here that, when dealing with data from long-term monitoring programmes, incorporating a temporal weight on the occurrence points may result in a more realistic prediction of a species' potential distribution. Using larval fish presence records collected from 1999 to 2013 in the Yangtze Estuary, China, we compared the performance of ensembles of standard SDMs versus SDMs constructed with weighted time-series presence records in predicting the present and future distributions of the larval stages of two dominant fish. The results of the ensemble SDMs showed that weighted presence records can significantly improve SDM performance, as measured through standard validation metrics. The SDM projections suggest that suitable habitat for both species will decrease under future climate scenarios, with one species (Stolephorus commersonnii) predicted to be more susceptible to climate change than the other (Engraulis japonicus). In addition to range contraction, model projections suggest that the future habitats of both species will shift northward—an implication of climate change that should be considered in future management and conservation strategies for the Yangtze Estuary.

Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species

Species distribution models (SDMs) correlate species occurrences with environmental predictors, and can be used to forecast distributions under future climates. SDMs have been criticized for not explicitly including the physiological processes underlying the species response to the environment. Recently, new methods have been suggested to combine SDMs with physiological estimates of performance (physiology‐SDMs). In this study, we compare SDM and physiology‐SDM predictions for select marine species in the Mediterranean Sea, a region subjected to exceptionally rapid climate change. We focused on six species and created physiology‐SDMs that incorporate physiological thermal performance curves from experimental data with species occurrence records. We then contrasted projections of SDMs and physiology‐SDMs under future climate (year 2100) for the entire Mediterranean Sea, and particularly the ‘warm’ trailing edge in the Levant region. Across the Mediterranean, we found cross‐validation model performance to be similar for regular SDMs and physiology‐SDMs. However, we also show that for around half the species the physiology‐SDMs substantially outperform regular SDM in the warm Levant. Moreover, for all species the uncertainty associated with the coefficients estimated from the physiology‐SDMs were much lower than in the regular SDMs. Under future climate, we find that both SDMs and physiology‐SDMs showed similar patterns, with species predicted to shift their distribution north‐west in accordance with warming sea temperatures. However, for the physiology‐SDMs predicted distributional changes are more moderate than those predicted by regular SDMs. We conclude, that while physiology‐SDM predictions generally agree with the regular SDMs, incorporation of the physiological data led to less extreme range shift forecasts. The results suggest that climate‐induced range shifts may be less drastic than previously predicted, and thus most species are unlikely to completely disappear with warming climate. Taken together, the findings emphasize that physiological experimental data can provide valuable supplemental information to predict range shifts of marine species.

Evaluating climatic threats to habitat types based on co-occurrence patterns of characteristic species

Species distribution models (SDMs) are used to project how suitable ranges of species shift under a warming climate. Conservation management, however, commonly targets habitat types rather than individual species. Such habitat types are often defined by the co-occurrence of a set of characteristic species. Here, we develop a co-occurrence-based index (CRI); which measures how the representation of habitat types in a particular area may change in a future climate. The index is based on stacking projections of distribution models of characteristic species and accounts for changes both in potential range size of each species individually and in spatial range overlap among characteristic species, i.e. co-occurrence patterns. We illustrate the approach by modelling the changing representation of 68 habitat types in Austria under two different climate scenarios. We base index calculations on SDM projections under either the assumption of unrestricted mobility (‘full-dispersal’) or of complete immobility (‘no-dispersal’) of individual species. Moreover, we compare results to those achieved with a simpler occurrence-based index (OI); which only accounts for change in species’ range sizes. All three alternative index calculations suggest that most modelled habitat types will lose area (in particular mires, wetlands and siliceous alpine grasslands) and only a minority will profit from a warming climate (in particular forests of dry and warm sites). ‘Full-dispersal’ CRI and OI are closely, but not perfectly correlated. Importantly, for more than half of the habitat types, accounting for changing co-occurrence patterns amplifies projected losses. The ‘no-dispersal’ CRI (CRInd) delivers the most alarming projections, indicating considerable spatial turn-over of sites suitable to the habitat types. Taken together, our results suggest that modelling re-distribution of habitat types which are defined by species combinations needs to account for modifications of co-occurrence patterns. Moreover, conservation should acknowledge that novel combinations of species will likely emerge under a warming climate.

Associations between habitat quality, body size and reproductive fitness in the alpine endemic spider Vesubia jugorum

AbstractAimIn theory, the highest‐quality habitat across a species’ range should support individuals with the highest fitness, making it possible to evaluate the outputs of species distribution models (SDMs) by exploring the relationship between habitat quality and functional traits related to species’ fitness. However, this relationship has been tested almost exclusively in plants. We investigated the degree to which morphological and reproductive traits of an alpine spider varied along a gradient of habitat quality projected via SDMs.LocationSouth‐western Alps (France and Italy).Time period2007–2018.Major taxa studiedVesubia jugorum (Arachnida: Araneae: Lycosidae).MethodsWe used climatic, topographical and geomorphological variables at a resolution of 250 m to model habitat quality for V. jugorum, with multiple algorithms (generalized additive models, boosted regression trees and maximum entropy models). We collected spiders in the field and measured their body size and egg‐case size (a functional trait related to reproductive success). We tested the relationship between functional traits and habitat quality with linear mixed models.ResultsAll SDM algorithms we tested fit the data well, with similar explanatory ability. Models revealed a positive relationship between the probability of presence and cumulative precipitation and percentage of rock. An additional important variable in the SDMs was duration of snow coverage, with optimal projected habitat quality between 40 and 100 days/year with snow. Modelled habitat quality was positively associated with maximum body and egg‐case size.Main conclusionsOur work provides evidence of a positive association between projected habitat quality and traits of a terrestrial invertebrate. A model that fits the data well potentially can be used to predict variations in species’ traits, thus offering an experimental test of SDM projections with field‐collected data. We encourage SDM users to incorporate data on functional traits into their modelling fitting exercises.

Input matters matter: Bioclimatic consistency to map more reliable species distribution models

Abstract Accuracy of global bioclimatic databases is essential to understand biodiversity–environment relationships. Many studies have explored biases and uncertainties related to species distribution models (SDMs) but the effect of choosing a specific database among the different alternatives has not been previously assessed. The lack of bioclimatic congruence (degree of agreement) between different databases is a main concern in distribution modelling and it is critical in single‐source models, for which the database choice is decisive. In order to prevent unreliable predictions derived from distorted input data, SDMs accuracy can be assessed by mapping model predictions according to a bioclimatic congruence measure derived from the comparison of multiple databases, which can be achieved with the bioclimatic consistency maps that we propose in this study. Here, (a) we present the first global‐scale bioclimatic congruence map to analyse environmental mismatches between recently updated bioclimatic databases. We also test the importance of input matters on the reliability of distribution models of sixteen mammals, by addressing (b) inconsistencies among species response curves (temperature and precipitation), and (c) discrepancies among SDMs predictions depending on the chosen bioclimatic database. Finally, (d) we propose a strategy to assess bioclimatic consistency of model predictions, showing its application to the specific case of Litocranius walleri. Our results confirm that the single‐source modelling approach greatly influences the estimation of species–environment relationship and consequently, bias spatial predictions derived from SDMs. This is especially true for studies conducted in polar and mountainous regions which showed the smallest bioclimatic congruence. We show that by adding bioclimatic congruence to SDMs projections, we can build a bioclimatic consistency map that enables the detection of both risky and consistent areas, as revealed for the case of L. walleri. Assessing uncertainty in bioclimatic input data is key to avoid erroneous conclusions in macroecological and biogeographical studies. The spatial characterization of bioclimatic consistency provides an adequate empirical framework which effectively illustrates bioclimatic data limitations. We strongly recommend that this new strategy should be formally and systematically incorporated into distribution modelling to build more reliable SDMs, which are essential to develop successful biodiversity conservation programmes.

Including environmental niche information to improve IUCN Red List assessments

AbstractAimInformation on change in species’ environmental preferences (i.e., niche) is currently not included in IUCN Red List criteria, although such information is key for assessing whether species not only lose geographic range but also lose part of their realized niche. Here, using niche size quantification and niche‐based species distribution models (SDMs), we test whether realized niche size and predicted potential range size provide additional information compared with the standard IUCN scores.LocationSwitzerland, national scale.MethodsWe simulated randomly, spatially directed, and ecologically directed local extinction events of varying magnitudes (10%, 30%, and 50% of occurrences). For a set of 148 representative vascular plant species, we tested how accurately the geographic versus niche measures pictured these extinction scenarios respectively.ResultsWe found that changes in niche size often corresponded to changes in geographic space. However, there was considerable variation and, for many species, changes in geographic and in niche space delivered complementary information. IUCN criteria based on spatial projections of SDMs did not capture extinction events in most cases and often increased the modelled range size, even when up to 50% of the occurrences were removed by simulated extinction events.Main conclusionOur findings demonstrate that changes in niche size can provide valuable additional information and could be used more systematically to complement changes in range size for Red List assessments. In turn, change in SDM‐predicted range size was not a good surrogate for classical extent of occurrence and area of occupancy criteria and should be used with caution. Further research is needed to assess whether and how spatial predictions of SDMs may be used to appropriately complement current IUCN criteria and to test whether our findings apply to other organisms and other spatial extents.

The contribution of cold air pooling to the distribution of a rare and endemic plant of the Alps

Background: The topographic complexity typical of alpine landscapes creates a variety of (micro)climatic conditions that may mitigate the effects of a warming climate on alpine plants via such mechanisms as cold air pooling (CAP).Aims: Our primary objectives were to (1) assess whether landscape potential for CAP as a predictor improved species distribution models (SDMs) projections and (2) quantify the impact of CAP on the microclimate experienced by alpine plants compared to the macroclimate.Methods: We selected the Maritime Alps as our study area, located on the French–Italian border, and its rare endemic plant, Saxifraga florulenta, as model taxon. We generated a spatial layer in GIS (Geographic Information System) that reflected the potential of the landscape for CAP and ran five SDM algorithms with and without CAP layer as a predictor. Second, we recorded the microclimate plants experience with temperature loggers.Results: CAP as a predictor decreased the omission error of SDMs, mostly at low and mid elevations, where topography may buffer extreme temperatures, resulting in a more stable microclimate compared to macroclimate.Conclusions: We have shown that plants in an alpine landscape may be less exposed to climate warming than predicted by macroclimate. Topo-climatic GIS layers for SDM projections in mountain environments should integrate such physical mechanisms as CAP.

Potential climate change effects on tree distributions in the Korean Peninsula: Understanding model & climate uncertainties

The projections of species distribution models (SDMs) have provided critical knowledge for conservation planning under climate change in the Republic of Korea. However, uncertainty about the SDM projections has been criticized as a major challenge to reliable projections. The present research investigated uncertainty among competing models (Model uncertainty) and uncertainty of future climate conditions (climate uncertainty) driving from different GCMs and CO2 emission scenarios in predicting the future distributions of plants. For this purpose, using nine single-model algorithms and the pre-evaluation weighted ensemble method, we modeled the geographical distributions of Silver Magnolia (Machilus thunbergii Siebold & Zucc.), a warm-adapted evergreen broadleaved tree; furthermore, we predicted its future distributions under 20 climate change scenarios (5 global circulation models (GCMs)×4 CO2 emission scenarios (RCPs)). The results showed a great variation in the accuracies of nine single-model projections: the mean AUC values of nine single-models ranged from 0.764 (SER) to 0.970 (RF), and the mean TSS ranged from 0.529 (SRE) to 0.852 (RF). RF (mean AUC=0.970, mean TSS=0.852) and the ensemble forecast (AUC=0.968, TSS=0.804) showed the highest predictive power, while SRE showed the lowest. The future distributions of Silver Magnolia projected with the ensemble SDM clearly varied according to GCMs and RCPs. The twenty climate scenarios produced twenty different projections of the magnolia prospective distribution. GCMs commonly projected the maximum range expansion under RCP 8.5 in 2050 and 2070, but CO2 emission scenarios explaining the minimum expansions differed according to GCMs. In conclusion, our results show that GCMs, CO2 emission scenarios and SDM algorithms produce considerable variations in the SDM projections. Therefore, this research suggests that understanding of model and climate uncertainties is critical for an effective conservation planning in forest management under climate change on the Korean Peninsula.

Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions

AbstractAimNiche‐based species distribution models (SDMs) are commonly used to predict impacts of global change on biodiversity, but the reliability of these predictions in space and time depends on their transferability. We tested how the strategy used to choose predictors impacts the transferability of SDMs at a cross‐continental scale.LocationNorth America, Eurasia and Australia.MethodWe used a systematic approach including 50 Holarctic plant invaders and 27 initial predictor variables, considering 10 different strategies for variable selection, accounting for the proximality, multicollinearity and climate analogy of predictors. We compared the average performance of each strategy, some of which used a large number of predictor combinations. Next, we looked for the single best model for each species across all the predictor combinations retained in the analysis. Transferability was considered as the predictive success of SDMs calibrated in the native range and projected onto the invaded range.ResultsTwo strategies showed better SDM transferability on average: a set of predictors known for their ecologically meaningful effects on plant distribution, and the two first axes of a principal component analysis calibrated on all predictor variables (Spc2). From the more than 2000 combinations of predictors per species across strategies, the best set of predictors yielded SDMs with good transferability for 45 species (90%). These best combinations consisted of eight randomly assembled (39 species) or uncorrelated predictors (6 species) and Spc2 (5 species). We also found that internal cross‐validation was not sufficient to give full information about the transferability of a SDM to a distinct range.Main conclusionTransferring SDMs at the macroclimatic scale, and thus anticipating invasions, is possible for the large majority of invasive plants considered in this study, but the accuracy of the predictions relies strongly on the choice of predictors. From our results, we recommend including either proximal and state‐of‐the‐art variables or a reduced and orthogonalized set to obtain robust SDM projections.

A model-based framework for assessing the vulnerability of low dispersal vertebrates to landscape fragmentation under environmental change

Environmental changes are driving rapid geographic shifts of suitable environmental conditions for species. These might survive by tracking those shifts, however successful responses will depend on the spatial distribution of suitable habitats (current and future) and on their connectivity. Most herptiles (i.e., amphibians and reptiles) have low dispersal abilities, and therefore herptiles are among the most vulnerable groups to environmental changes. Here we assessed the vulnerability of herptile species to future climate and land use changes in fragmented landscapes. We developed and tested a methodological approach combining the strengths of Species Distribution Models (SDMs) and of functional connectivity analysis. First, using SDMs we forecasted current and future distributions of potential suitable areas as well as range dynamics for four herptile species in Portugal. SDM forecasts for 2050 were obtained under two contrasting emission scenarios, translated into moderate (low-emissions scenario) or large (high-emissions scenario) changes in climate and land use conditions. Then, we calculated and analysed functional connectivity from areas projected to lose environmental suitability towards areas keeping suitable conditions. Landscape matrix resistance and barrier effects of the national motorway network were incorporated as the main sources of fragmentation. Potential suitable area was projected to decrease under future conditions for most test species, with the high-emissions scenario amplifying the losses or gains. Spatiotemporal patterns of connectivity between potentially suitable areas signalled the most important locations for maintaining linkages and migration corridors, as well as potential conflicts due to overlaps with the current motorway network. By integrating SDM projections with functional connectivity analysis, we were able to assess and map the vulnerability of distinct herptile species to isolation or extinction under environmental change scenarios. Our framework provides valuable information, with fairly low data requirements, for optimizing biodiversity management and mitigation efforts, aiming to reduce the complex and often synergistic negative impacts of multiple environmental change drivers. Implications for conservation planning and management are discussed from a global change adaptation perspective.