Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.     

How to deal with ground truthing affected by human‐induced habitat change?: Identifying high‐quality habitats for the Critically Endangered Red Siskin

Species distribution models (SDM) can be valuable for identifying key habitats for conservation management of threatened taxa, but anthropogenic habitat change can undermine SDM accuracy. We used data for the Red Siskin (Spinus cucullatus), a critically endangered bird and ground truthing to examine anthropogenic habitat change as a source of SDM inaccuracy. We aimed to estimate: (1) the Red Siskin's historic distribution in Venezuela; (2) the portion of this historic distribution lost to vegetation degradation; and (3) the location of key habitats or areas with both, a high probability of historic occurrence and a low probability of vegetation degradation. We ground‐truthed 191 locations and used expert opinion as well as landscape characteristics to classify species' habitat suitability as excellent, good, acceptable, or poor. We fit a Random Forest model (RF) and Enhanced Vegetation Index (EVI) time series to evaluate the accuracy and precision of the expert categorization of habitat suitability. We estimated the probability of historic occurrence by fitting a MaxLike model using 88 presence records (1960–2013) and data on forest cover and aridity index. Of the entire study area, 23% (20,696 km²) had a historic probability of Red Siskin occurrence over 0.743. Furthermore, 85% of ground‐truthed locations had substantial reductions in mean EVI, resulting in key habitats totaling just 976 km², in small blocks in the western and central regions. Decline in Area of Occupancy over 15 years was between 40% and 95%, corresponding to an extinction risk category between Vulnerable and Critically Endangered. Relating key habitats with other landscape features revealed significant risks and opportunities for proposed conservation interventions, including the fact that ongoing vegetation degradation could limit the establishment of reintroduced populations in eastern areas, while the conservation of remaining key habitats on private lands could be improved with biodiversity‐friendly agri‐ and silviculture programs.     

Refugee species: which historic baseline should inform conservation planning?

Understanding species' historical ranges can provide important information for conservation planning in the face of environmental change. Cromsigt et al. (this issue) comment on our recent European bison (Bison bonasus) range reconstruction, suggesting that bison were already 8000 years ago a refugee species (i.e. restricted to marginal habitat due to past human pressure) and that species distribution models (SDM) are generally of limited use for refugee species conservation. While we welcome this discussion, we find no evidence for the claim that human pressure prior to 8000 BP determined where bison occurred. More importantly, as human pressure is generally high and increasing, attempts to restore species across their former range may fail where the factors that relegated species into refugee status are still at play or where their optimal habitat has vanished. Identifying areas where human pressure is low and where refugee species have persisted over the last millennia is crucial, and SDM based on historical data are important for doing so. Refugee species suffer from the shifting baseline syndrome, but careful reality checks are needed and all available data should be considered before determining the baseline that should inform conservation planning.     

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long‐term stable habitats. The variability of complex, short‐term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.     

Conservation implications of the refugee species concept and the European bison: king of the forest or refugee in a marginal habitat?

The concept of refugee species provides a theoretical framework towards increasing the predictive power of the ‘declining population paradigm’ through identifying species which are expected to suffer from a declining population syndrome. Using a simple habitat model as a framework, refugee species are defined as those that can no longer access optimal habitat, but are confined to suboptimal habitats, with consequences of decreased fitness and density, and attendant conservation risks. Refugee species may be difficult to detect in the absence of information on prior habitat use and fitness and their observed ecology will be constrained by the habitat limits forced on them. Identification of refugee species, characterisation of pre‐refugee ecology and the restoration of such species to optimal habitat is critical to their successful conservation. The concept is showcased by addressing the conundrum of a large grazing bovid, the European bison Bison bonasus, being managed as a forest specialist, despite its evolutionary background, dental morphology, neonatal behaviour, diet and microhabitat selection being characteristic of a grazing species inhabiting open, grass‐rich habitats. It is hypothesized that a combination of increasing replacement of open steppe by forest cover after the last postglacial period and increasing human pressure forced bison into forests as a refuge habitat. This process was then reinforced through active management of bison in forests as managers committed themselves to the ‘bison as forest species’ paradigm. A research agenda to test this hypothesis using an experimental approach in the conservation management of European bison by introducing populations into diverse habitat types is suggested.     

Habitat suitability modelling for species at risk is sensitive to algorithm and scale: A case study of Blanding's turtle,Emydoidea blandingii,in Ontario,Canada

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boosted regression trees; and maximum entropy modelling. The area under the receiver characteristic operating curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912 when using random background and from 0.727 to 0.741 with target-group background. E. blandingii habitat suitability was best predicted by air temperature, wetland area, open water area, road density, and cropland area. Habitat suitability increased with increasing air temperature and wetland area, and decreased with increasing cropland area. Low road density and open water increased habitat suitability, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps for species at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offer insight on the habitat requirements of species at risk and help guide the development of management plans. Our results suggest that E. blandingii management plans should promote the protection of terrestrial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currently occupied habitat, and identify movement corridors for isolated populations.     

An anthropogenic habitat within a suboptimal colonized ecosystem provides improved conditions for a range‐shifting species

Many species are shifting their ranges in response to the changing climate. In cases where such shifts lead to the colonization of a new ecosystem, it is critical to establish how the shifting species itself is impacted by novel environmental and biological interactions. Anthropogenic habitats that are analogous to the historic habitat of a shifting species may play a crucial role in the ability of that species to expand or persist in suboptimal colonized ecosystems. We tested if the anthropogenic habitat of docks, a likely mangrove analog, provides improved conditions for the range‐shifting mangrove tree crab Aratus pisonii within the colonized suboptimal salt marsh ecosystem. To test if docks provided an improved habitat, we compared the impact of the salt marsh and dock habitats on ecological and life history traits that influence the ability of this species to persist and expand into the salt marsh and compared these back to baselines in the historic mangrove ecosystem. Specifically, we examined behavior, physiology, foraging, and the thermal conditions of A. pisonii in each habitat. We found that docks provide a more favorable thermal and foraging habitat than the surrounding salt marsh, while their ability to provide conditions which improved behavior and physiology was mixed. Our study shows that anthropogenic habitats can act as analogs to historic ecosystems and enhance the habitat quality for range‐shifting species in colonized suboptimal ecosystems. If the patterns that we document are general across systems, then anthropogenic habitats may play an important facilitative role in the range shifts of species with continued climate change.     

Predicting habitat distribution and frequency from plant species co-occurrence data

Aim Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid‐based habitat occurrence from grid‐based plant species distribution data in a meso‐scale analysis. Location The study was carried out over two spatial extents: Germany and Bavaria. Methods Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de ). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Results Using the Bavarian models it was possible to predict habitat distribution and frequency from the co‐occurrence of habitat‐specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat‐specific species co‐occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co‐occurrence of 24% of formation‐specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. Main conclusions It was concluded that it is possible to deduce habitat distributions and frequencies from the co‐occurrence of habitat‐specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set‐up.     

Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user‐friendly, high‐resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high‐resolution and readily usable package of global environmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite‐based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio‐ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user‐friendly data package for marine species distribution modelling is available for download at http://www.bio‐oracle.ugent.be . The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow‐water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence‐only model of a notorious invasive seaweed shows that the information contained in Bio‐ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.     

Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin-wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty-nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat-based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate-only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management.     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

     

Species distribution models as a tool to estimate reproductive parameters: a case study with a passerine bird species

1.?Correlative species distribution models (SDMs) assess relationships between species distribution data and environmental features, to evaluate the environmental suitability (ES) of a given area for a species, by providing a measure of the probability of presence. If the output of SDMs represents the relationships between habitat features and species performance well, SDM results can be related also to other key parameters of populations, including reproductive parameters. To test this hypothesis, we evaluated whether SDM results can be used as a proxy of reproductive parameters (breeding output, territory size) in red-backed shrikes (Lanius collurio). 2.?The distribution of 726 shrike territories in Northern Italy was obtained through multiple focused surveys; for a subset of pairs, we also measured territory area and number of fledged juveniles. We used Maximum Entropy modelling to build a SDM on the basis of territory distribution. We used generalized least squares and spatial generalized mixed models to relate territory size and number of fledged juveniles to SDM suitability, while controlling for spatial autocorrelation. 3.?Species distribution models predicted shrike distribution very well. Territory size was negatively related to suitability estimated through SDM, while the number of fledglings significantly increased with the suitability of the territory. This was true also when SDM was built using only spatially and temporally independent data. 4.?Results show a clear relationship between ES estimated through presence-only SDMs and two key parameters related to species' reproduction, suggesting that suitability estimated by SDM, and habitat quality determining reproduction parameters in our model system, are correlated. Our study shows the potential use of SDMs to infer important fitness parameters; this information can have great importance in management and conservation.     

Detecting outliers in species distribution data

Aim

Species distribution data play a pivotal role in the study of ecology, evolution, biogeography and biodiversity conservation. Although large amounts of location data are available and accessible from public databases, data quality remains problematic. Of the potential sources of error, positional errors are critical for spatial applications, particularly where these errors place observations beyond the environmental or geographical range of species. These outliers need to be identified, checked and removed to improve data quality and minimize the impact on subsequent analyses. Manually checking all species records within large multispecies datasets is prohibitively costly. This work investigates algorithms that may assist in the efficient vetting of outliers in such large datasets.

Location

We used real, spatially explicit environmental data derived from the western part of Victoria, Australia, and simulated species distributions within this same region.

Methods

By adapting species distribution modelling (SDM), we developed a pseudo‐SDM approach for detecting outliers in species distribution data, which was implemented with random forest (RF) and support vector machine (SVM) resulting in two new methods: RF_pdSDM and SVM_pdSDM. Using virtual species, we compared eight existing multivariate outlier detection methods with these two new methods under various conditions.

Results

The two new methods based on the pseudo‐SDM approach had higher true skill statistic (TSS) values than other approaches, with TSS values always exceeding 0. More than 70% of the true outliers in datasets for species with a low and intermediate prevalence can be identified by checking 10% of the data points with the highest outlier scores.

Main conclusions

Pseudo‐SDM‐based methods were more effective than other outlier detection methods. However, this outlier detection procedure can only be considered as a screening tool, and putative outliers must be examined by experts to determine whether they are actual errors or important records within an inherently biased set of data.     

Incorporating microclimate into species distribution models

Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.     

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.     

Performance of a species distribution model based on presence/absence for various population sizes in the amphidromous fish species Sicyopterus japonicus

We formulated a species distribution model (SDM) for the amphidromous fish Sicyopterus japonicus on the basis of presence/absence data and confirmed its performance at various population densities. The best-fit SDM selected several environmental factors, including water depth and velocity, and performed validated prediction of presence/absence of various densities. The predicted probability of occurrence was positively correlated with the observed density. The density was positively related to habitat occupancy, suggesting that this species occupied a wide range of habitats under high densities, but only optimal habitat under low densities. Therefore, the threshold value for predicting presence/absence increased with decreasing densities.     

Landscape effects on gene flow for a climate‐sensitive montane species,the American pika

Climate change is arguably the greatest challenge to conservation of our time. Most vulnerability assessments rely on past and current species distributions to predict future persistence but ignore species' abilities to disperse through landscapes, which may be particularly important in fragmented habitats and crucial for long‐term persistence in changing environments. Landscape genetic approaches explore the interactions between landscape features and gene flow and can clarify how organisms move among suitable habitats, but have suffered from methodological uncertainties. We used a landscape genetic approach to determine how landscape and climate‐related features influence gene flow for American pikas (Ochotona princeps) in Crater Lake National Park. Pikas are heat intolerant and restricted to cool microclimates; thus, range contractions have been predicted as climate changes. We evaluated the correlation between landscape variables and genetic distance using partial Mantel tests in a causal modelling framework, and used spatially explicit simulations to evaluate methods of model optimization including a novel approach based on relative support and reciprocal causal modelling. We found that gene flow was primarily restricted by topographic relief, water and west‐facing aspects, suggesting that physical restrictions related to small body size and mode of locomotion, as well as exposure to relatively high temperatures, limit pika dispersal in this alpine habitat. Our model optimization successfully identified landscape features influencing resistance in the simulated data for this landscape, but underestimated the magnitude of resistance. This is the first landscape genetic study to address the fundamental question of what limits dispersal and gene flow in the American pika.     

Living quarters of a living fossil—Uncovering the current distribution pattern of the rediscovered Hula painted frog (Latonia nigriventer) using environmental DNA

One of the greatest challenges of effective conservation measures is the correct identification of sites where rare and elusive organisms reside. The recently rediscovered Hula painted frog (Latonia nigriventer) has not been seen for many decades and was therefore categorized extinct. Since its rediscovery in 2011, individuals from the critically endangered species have been found, with great effort, only in four restricted sites. We applied the environmental DNA (eDNA) approach to search for new populations of the Hula painted frog in suitable aquatic habitats. We further used the eDNA data to classify the landscape factors associated with the species distribution and to predict its suitable habitats. We sampled 52 aquatic sites in the Hula Valley during the spring of 2015 and 2016 and amplified the samples with a species‐specific qPCR assay. DNA of the Hula painted frog was detected in 22 of the sites, all of which clustered within three main areas. A boosting classification model showed that soil type, vegetation cover and the current and former habitats are all key predictors of the frog's current distribution. Intriguingly, the habitat suitability models reveal a high affinity of the species to its long‐lost habitat of the historical wetlands. Our findings encourage a series of informed searches for new populations of this threatened frog and provide guidance for future conservation management programmes. In the era of global conservation crisis of amphibians, developing the eDNA approach, a reliable detection method for many critically endangered and elusive amphibians, is of particular importance.     

Phylogeography,population genetics and distribution modelling reveal vulnerability of Scirpus longii (Cyperaceae) and the Atlantic Coastal Plain Flora to climate change

A proactive approach to conservation must be predictive, anticipating how habitats will change and which species are likely to decline or prosper. We use composite species distribution modelling to identify suitable habitats for 18 members of the North American Atlantic Coastal Plain Flora (ACPF) since the Last Glacial Maximum and project these into the future. We then use Scirpus longii (Cyperaceae), a globally imperiled ACPF sedge with many of the characteristics of extinction vulnerability, as a case study. We integrate phylogeographical and population genetic analyses and species distribution modelling to develop a broad view of its current condition and prognosis for conservation. We use genotyping‐by‐sequencing to characterize the genomes of 142 S. longii individuals from 20 populations distributed throughout its range (New Jersey to Nova Scotia). We measure the distribution of genetic diversity in the species and reconstruct its phylogeographical history using the snapp and rase models. Extant populations of S. longii originated from a single refugium south of the Laurentide ice sheet around 25 ka. The genetic diversity of S. longii is exceedingly low, populations exhibit little genetic structure and the species is slightly inbred. Projected climate scenarios indicate that nearly half of extant populations of S. longii will be exposed to unsuitable climate by 2070. Similar changes in suitable habitat will occur for many other northern ACPF species—centres of diversity will shift northward and Nova Scotia may become the last refuges for those species not extinguished.     

Using species distribution models to define nesting habitat of the eastern metapopulation of double‐crested cormorants

When organisms with similar phenotypes have conflicting management and conservation initiatives, approaches are needed to differentiate among subpopulations or discrete groups. For example, the eastern metapopulation of the double‐crested cormorant (Phalacrocorax auritus) has a migratory phenotype that is culled because they are viewed as a threat to commercial and natural resources, whereas resident birds are targeted for conservation. Understanding the distinct breeding habitats of resident versus migratory cormorants would aid in identification and management decisions. Here, we use species distribution models (SDM: Maxent) of cormorant nesting habitat to examine the eastern P. auritus metapopulation and the predicted breeding sites of its phenotypes. We then estimate the phenotypic identity of breeding colonies of cormorants where management plans are being developed. We transferred SDMs trained on data from resident bird colonies in Florida and migratory bird colonies in Minnesota to South Carolina in an effort to identify the phenotype of breeding cormorants there based on the local landscape characteristics. Nesting habitat characteristics of cormorant colonies in South Carolina more closely resembled those of the Florida phenotype than those of birds of the Minnesota phenotype. The presence of the resident phenotype in summer suggests that migratory and resident cormorants will co‐occur in South Carolina in winter. Thus, there is an opportunity for separate management strategies for the two phenotypes in that state. We found differences in nesting habitat characteristics that could be used to refine management strategies and reduce human conflicts with abundant winter migrants and, at the same time, conserve less common colonies of resident cormorants. The models we use here show potential for advancing the study of geographically overlapping phenotypes with differing conservation and management priorities.