Species Distribution Models Outputs 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.

Performance of inhomogeneous Poisson point process models under different scenarios of uncertainty in species presence-only data

Haphazard and opportunistic species occurrence (PO) data are widely used in species distribution models (SDMs) instead of high-quality species data gathered using appropriate and structured sampling methods, which is expensive and often spatially limited. Despite their widespread use in ecology, PO data are prone to errors and uncertainties, such as imperfect detectability, positional imprecision, and spatial niche truncation, which make their use analytically challenging for effective and adaptive biodiversity management and conservation. Using simulated data, this study investigates the effects of these uncertainties on the performance of spatial point process based presence-only and integrated SDMs. We investigated three SDMs in this study, one that ignores imperfect detectability: the presence-only model (PO model), and two that account for it: the thinned presence-only model (THINPO model) and the integrated model (PBPC model). The ability of these SDMs to produce accurate maximum likelihood estimates of intensity model coefficients and reliable predictions of species distributions under different data quality scenarios was investigated. The results show that SDMs that account for imperfect detectability (THINPO or PBPC models) are not applicable in situations of high detectability. In this situation, the PO model produces the most accurate maximum likelihood estimates of the models’ coefficients ({hat{beta }}_k), and consequently the most accurate predictions of species distributions ({hat{lambda }}(s)). The effects of positional uncertainty and spatial niche truncation on this SDM output are minimal. However, in situations of low detectability, it is preferable to use the PBPC model. Positional uncertainty and spatial niche truncation have negligible effects on the output of this SDM, except when positionally uncertain PO data are analyzed along with truncated PC data. These minimal effects of spatial niche truncation on SDM outputs demonstrate the transferability of SDMs. However, the effects of all these uncertainties may depend on the characteristics of the species. Prior to modeling species distributions, a multivariate environmental similarity surface analysis should be performed to test the similarity between data from the restricted region to be used for model calibration and data from the entire range. If this analysis reveals dissimilarities, larger spatial and ecological scales should be considered to address the issue of spatial niche truncation. Further efforts could address the effects of species characteristics on SDMs performance and assess the effects of species-specific uncertainties.

Considering the implications of climate-induced species range shifts in marine protected areas planning

Climate change is affecting the ocean, altering the biogeography of marine species. Yet marine protected area (MPA) planning still rarely incorporates projected species range shifts. We used the outputs of species distribution models fitted with biological and climate data as inputs to identify trends in occurrence for marine species in British Columbia (BC), Canada. We assessed and compared two ways of incorporating climate change projections into MPA planning. First, we overlaid 98 species with modelled distributions now and by the mid-21st century under two contrasting (“no mitigation” and “strong mitigation”) climate change scenarios with existing Provincial marine parks in BC, to ask which species could overlap with protected areas in the future. Second, we completed a spatial prioritization analysis using Marxan with the projected future species ranges as inputs, to ask where priority regions exist for the 98 marine species. We found that many BC marine parks will lose species in both climate scenarios that we analyzed, and that protecting 30% of important marine species will be challenging under the “no mitigation” climate change scenario. Challenges included the coarse resolution of the data and uncertainty in projecting species range shifts.

Improving predictions of coastal benthic invertebrate occurrence and density using a multi-scalar approach

Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Can We Avoid Tacit Trade-Offs between Flexibility and Efficiency in Systematic Conservation Planning? The Mediterranean Sea as a Case Study

Species distribution models (SDMs) provide robust inferences about species-specific site suitability and are increasingly used in systematic conservation planning (SCP). SDMs are subjected to intrinsic uncertainties, and conservation studies have generally overlooked these. The integration of SDM uncertainties in conservation solutions requires the development of a suitable optimization algorithm. Exact optimization algorithms grant efficiency to conservation solutions, but most of their implementations generate a single binary and indivisible solution. Therefore, without variation in their parameterization, they provide low flexibility in the implementation of conservation solutions by stakeholders. Contrarily, heuristic algorithms provide such flexibility, by generating large amounts of sub-optimal solutions. As a consequence, efficiency and flexibility are implicitly linked in conservation applications: mathematically efficient solutions provide less flexibility, and the flexible solutions provided by heuristics are sub-optimal. To avoid this trade-off between flexibility and efficiency in SCP, we propose a reserve-selection framework, based on exact optimization combined with a post-selection of SDM outputs. This reserve-selection framework provides flexibility and addresses the efficiency and representativeness of conservation solutions. To exemplify the approach, we analyzed an experimental design, crossing pre- and post-selection of SDM outputs versus heuristics and exact mathematical optimizations. We used the Mediterranean Sea as a biogeographical template for our analyses, integrating the outputs of eight SDM techniques for 438 fish species.

Broad‐scale patterns of geographic avoidance between species emerge in the absence of fine‐scale mechanisms of coexistence

AbstractAimThe need to forecast range shifts under future climate change has motivated an increasing interest in better understanding the role of biotic interactions in driving diversity patterns. The contribution of biotic interactions to shaping broad‐scale species distributions is, however, still debated, partly due to the difficulty of detecting their effects. We aim to test whether spatial exclusion between potentially competing species can be detected at the species range scale, and whether this pattern relates to fine‐scale mechanisms of coexistence.LocationWestern Palearctic.MethodsWe develop and evaluate a measure of geographic avoidance that uses outputs of species distribution models to quantify geographic exclusion patterns expected if interspecific competition affects broad‐scale distributions. We apply the measure to 10 Palearctic bat species belonging to four morphologically similar cryptic groups in which competition is likely to occur. We compare outputs to null models based on pairs of virtual species and to expectations based on ecological similarity and fine‐scale coexistence mechanisms. We project changes in range suitability under climate change taking into account effects of geographic avoidance.ResultsValues of geographic avoidance were above null expectations for two cryptic species pairs, suggesting that interspecific competition could have contributed to shaping their broad‐scale distributions. These two pairs showed highest levels of ecological similarity and no trophic or habitat partitioning. Considering the role of competition modified predictions of future range suitability.Main conclusionsOur results support the role of interspecific competition in limiting the geographic ranges of morphologically similar species in the absence of fine‐scale mechanisms of coexistence. This study highlights the importance of incorporating biotic interactions into predictive models of range shifts under climate change, and the need for further integration of community ecology with species distribution models to understand the role of competition in ecology and biogeography.

Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data

Abstract Spatial biases are a common feature of presence–absence data from citizen scientists. Spatial thinning can mitigate errors in species distribution models (SDMs) that use these data. When detections or non‐detections are rare, however, SDMs may suffer from class imbalance or low sample size of the minority (i.e. rarer) class. Poor predictions can result, the severity of which may vary by modelling technique. To explore the consequences of spatial bias and class imbalance in presence–absence data, we used eBird citizen science data for 102 bird species from the northeastern USA to compare spatial thinning, class balancing and majority‐only thinning (i.e. retaining all samples of the minority class). We created SDMs using two parametric or semi‐parametric techniques (generalized linear models and generalized additive models) and two machine learning techniques (random forest and boosted regression trees). We tested the predictive abilities of these SDMs using an independent and systematically collected reference dataset with a combination of discrimination (area under the receiver operator characteristic curve; true skill statistic; area under the precision‐recall curve) and calibration (Brier score; Cohen's kappa) metrics. We found large variation in SDM performance depending on thinning and balancing decisions. Across all species, there was no single best approach, with the optimal choice of thinning and/or balancing depending on modelling technique, performance metric and the baseline sample prevalence of species in the data. Spatially thinning all the data was often a poor approach, especially for species with baseline sample prevalence <0.1. For most of these rare species, balancing classes improved model discrimination between presence and absence classes using machine learning techniques, but typically hindered model calibration. Baseline sample prevalence, sample size, modelling approach and the intended application of SDM output—whether discrimination or calibration—should guide decisions about how to thin or balance data, given the considerable influence of these methodological choices on SDM performance. For prognostic applications requiring good model calibration (vis‐à‐vis discrimination), the match between sample prevalence and true species prevalence may be the overriding feature and warrants further investigation.

Leaving the area under the receiving operating characteristic curve behind: An evaluation method for species distribution modelling applications based on presence‐only data

Abstract The area under the curve (AUC) of the receiving‐operating characteristic (or certain modifications of it) is almost universally used to assess the performance of species distribution models (SDMs), despite the well‐recognized problems encountered with this approach, mainly present when dealing with presence‐only data. We present a probabilistic treatment of the presence‐only problem and derive a method to assess the performance of SDMs based on the analysis of an area‐presence plot and the SDM outputs represented in both geographic and environmental spaces. We show how our method is useful to solve the two main tasks for which the AUC is used: assessing the performance of an SDM and comparing the performance of different SDMs. Our results build on previous work and constitute a rigorous method for assessing the performance of SDMs in relation to a random classifier. We establish comparisons with two of the most popular approaches used to assess the performance of an SDM, the AUC and the Boyce index, and identified cases in which our method has advantages over these two approaches. We suggest that the performance of an algorithm that classifies presence‐only data can be assessed by two factors: (a) the degree of non‐randomness of the classification at every step in the accumulation curve of presences, and (b) the amount of uninformative niche space used for the classification. The method we developed can be applied to any SDM output by using the R functions available at: https://github.com/LauraJim/SDM‐hyperTest.

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

AbstractHabitat 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.

Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom.

Vector borne diseases are a continuing global threat to both human and animal health. The ability of vectors such as mosquitos to cover large distances and cross country borders undetected provide an ever-present threat of pathogen spread. Many diseases can infect multiple vector species, such that even if the climate is not hospitable for an invasive species, indigenous species may be susceptible and capable of transmission such that one incursion event could lead to disease establishment in these species. Here we present a consensus modelling methodology to estimate the habitat suitability for presence of mosquito species in the UK deemed competent for Rift Valley fever virus (RVF) and demonstrate its application in an assessment of the relative risk of establishment of RVF virus in the UK livestock population. The consensus model utilises observed UK mosquito surveillance data, along with climatic and geographic prediction variables, to inform six independent species distribution models; the results of which are combined to produce a single prediction map. As a livestock host is needed to transmit RVF, we then combine the consensus model output with existing maps of sheep and cattle density to predict the areas of the UK where disease is most likely to establish in local mosquito populations. The model results suggest areas of high suitability for RVF competent mosquito species across the length and breadth of the UK. Notable areas of high suitability were the South West of England and coastal areas of Wales, the latter of which was subsequently predicted to be at higher risk for establishment of RVF due to higher livestock densities. This study demonstrates the applicability of outputs of species distribution models to help predict hot-spots for risk of disease establishment. While there is still uncertainty associated with the outputs we believe that the predictions are an improvement on just using the raw presence points from a database alone. The outputs can also be used as part of a multidisciplinary approach to inform risk based disease surveillance activities.

Expanding ensembles of species present-day and future climatic suitability to consider the limitations of species occurrence data

Abstract Outputs of species distribution models (SDMs) are widely used as indicators of climate conditions favorable for species occurrence. When using these outputs to inform planning and decision making, it is essential that the uncertainties associated with the projections of present-day and future climatic suitability are carefully considered. Climate change assessments routinely consider the uncertainty introduced into SDM outputs by differences in future climate projections, and other uncertainty sources, such as the choice of the threshold to convert simulated probabilities to binary climatically suitable areas, are also oftentimes considered. However, the uncertainty introduced by the limitations of the species occurrence data used in the SDM calibration is rarely evaluated. These limitations, which include location error, sampling bias, and species misidentification, may reduce the utility of SDM outputs in conservation research and practice. Using understory bamboo species in southwest China as examples, here we demonstrate that species occurrences obtained using remote sensing offer an additional dataset for calibrating SDMs that, in conjunction with conventional observations and employing an ensemble approach of outputs from multiple models, provide an estimate of the uncertainty introduced by the species occurrence data. A biweekly time series of the satellite-based Wide Dynamic Range Vegetation Index (WDRI) was employed to estimate bamboo occurrence based on phenological signatures of the bamboo species and their overstory canopies. Using Maxent, a popular modeling framework, present-day and projected future climatic suitability were assessed separately for conventional species presence observations from the Fourth National Giant Panda Survey and for the remotely-sensed presence estimates. The ensemble of model outputs suggests that the uncertainty introduced by the species occurrence data, along with the interaction with other sources of uncertainty, may be as substantial as the uncertainty introduced by the use of different climate scenarios or by the threshold used to estimate binary climatically suitable areas. Ignoring the uncertainty introduced by the limitations of the species occurrences may compromise the interpretation of SDM outputs and reduce their usefulness for conservation planning. Remote sensing is a largely untapped resource for assessing uncertainty in SDM simulations.

Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic‐alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific‐level SDMs with a species‐level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic‐ and habitat‐informed SDMs are considerably more accurate than a species‐level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific‐level SDMs. We emphasize the need to carefully examine how to best define intraspecific‐level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often‐assumed relationships are not supported in our study.

Shifted distribution baselines: neglecting long-term biodiversity records risks overlooking potentially suitable habitat for conservation management.

Setting appropriate conservation measures to halt the loss of biodiversity requires a good understanding of species' habitat requirements and potential distribution. Recent (past few decades) ecological data are typically used to estimate and understand species' ecological niches. However, historical local extinctions may have truncated species-environment relationships, resulting in a biased perception of species' habitat preferences. This may result in incorrect assessments of the area potentially available for their conservation. Incorporating long-term (centuries-old) occurrence records with recent records may provide better information on species-environment relationships and improve the modelling and understanding of habitat suitability. We test whether neglecting long-term occurrence records leads to an underestimation of species' historical niche and potential distribution and identify which species are more vulnerable to this effect. We compare outputs of species distribution models and niche hypervolumes built using recent records only with those built using both recent and long-term (post-1500) records, for a set of 34 large mammal species in South Africa. We find that, while using recent records only is adequate for some species, adding historical records in the analyses impacts estimates of the niche and habitat suitability for 12 species (34%) in our dataset, and that this effect is significantly higher for carnivores. These results show that neglecting long-term biodiversity records in spatial analyses risks misunderstanding, and generally underestimating, species' niches, which in turn may lead to ill-informed management decisions, with significant implications for the effectiveness of conservation efforts. This article is part of a discussion meeting issue 'The past is a foreign country: how much can the fossil record actually inform conservation?'

NOO3D: A procedure to perform 3D species distribution models

There is consensus surrounding the need to include a third dimension when estimating Species Distribution Models (SDMs), which is of special interest for marine species. Application of the third dimension is, however, rarely available, thus users are obliged to manually combine 2D SDM outputs (i.e., suitability or presence/absence maps) for 3D distribution generation. Herein, the Niche of Occurrence 3D (NOO3D) is presented, which is a new, simple modelling procedure that provides 3D distributions using both 3D occurrence samples and environmental datasets that consist of one layer per depth value. NOO3D performance was evaluated using five virtual marine species to avoid errors associated with real data sets (three pelagic species, with wide, medium, and narrow distributions, respectively, a mesopelagic species and an abyssal species). These virtual species are distributed across the North Atlantic Ocean and were built to a 0.5° x 0.5° resolution and considering 49 depth levels (from 0.43 m to an undersea depth of 5274.7 m). NOO3D results were also compared to those provided by 3D Alpha Shapes and Maximum Entropy (MaxEnt). The True Positive Rate (TPR), or sensitivity, True Negative Rate (TNR), or specificity, False Positive Rate (FPR), or commission error, and False Negative Rate (FNR), or omission error, were employed in order to facilitate comparison between methods. MaxEnt performed best for TPR, TSS and FNR, and Alpha Shape 3D performed best for FPR and TNR. NOO3D was always the second-ranked method for all metrics considered, which indicates that it was the most suitable method. The provided results indicate that NOO3D can be considered a viable alternative in achieving three-dimensional species distribution models.

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.

The role of protected areas in preserving habitat and functional connectivity for mobile flying vertebrates: the common noctule bat (Nyctalus noctula) in Tuscany (Italy) as a case study

Preserving species within protected areas (PAs) does not guarantee adequate levels of protection if not coupled with conservation of functional connectivity for a target species. We propose an analytical framework to assess the effectiveness of PAs in preserving habitat and functional connectivity for mobile vertebrates. We implemented it in central Italy by using as a case study a bat species (common noctule, Nyctalus noctula) to: (i) determine suitable areas by means of Species Distribution Models (SDMs); (ii) identify potential commuting corridors through a functional connectivity analysis; (iii) develop a new tool to rank corridors according to their functional irreplaceability; (iv) implement a gap analysis on both suitable areas and functional corridors; and (v) propose management recommendations for the conservation of N. noctula. The SDM output and a set of proxies of commuting routes were used to build a resistance layer for the connectivity analysis. The resulting functional corridors were ranked according to their isolation (distance to other corridors and to suitable areas) to obtain an irreplaceability index, with isolated corridors scoring high values. The PA effectiveness assessed by overlapping the PA map with the SDM and the ranked functional corridors highlighted that PAs cover just a small portion of suitable sites (20.3%) and functional corridors for the species (20.8%). The irreplaceability index allowed us to identify those areas inside and outside the PAs that critical for persistence of the species in question require immediate protection regimes. The approach we present could be easily extended to other taxa and offers sound insight on how to promote the conservation at landscape scale.

Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats

Phenology is a key feature in the description of species niches to capture seasonality in resource use and climate requirements. Species distribution models (SDMs) are widespread tools to evaluate a species’ potential distribution and identify its large-scale habitat preferences. Despite its chief importance, data phenology is often neglected in SDM development. Non-migratory bats of temperate regions are good model species to test the effect of data seasonality on SDM outputs because of their different roosting preferences between hibernation and reproduction. We hypothesized that (1) the output of SDMs developed for six non-migratory European bat species will differ between hibernation and reproduction; (2) models built from datasets encompassing both ecological stages will perform better than seasonal models. We employed a dataset of 470 independent occurrences of bat hibernacula and 400 independent records of nursery roosts of selected species and for each species we developed separate winter, summer and mixed (i.e. generated from both winter and summer occurrences) models. Seasonal and mixed potential ranges differed from each other and the direction of this difference was species-specific. Mixed models outperformed seasonal models in representing species niches. Our work highlights the importance of considering data seasonality in the development of SDMs for bats as well as many other organisms, including non-migratory species, otherwise the analysis will lead to significant biases whose consequences for conservation planning and landscape management may be detrimental.

Unraveling climate influences on the distribution of the parapatric newts Lissotriton vulgaris meridionalis and L. italicus

BackgroundClimate is often considered as a key ecological factor limiting the capability of expansion of most species and the extent of suitable habitats. In this contribution, we implement Species Distribution Models (SDMs) to study two parapatric amphibians, Lissotriton vulgaris meridionalis and L. italicus, investigating if and how climate has influenced their present and past (Last Glacial Maximum and Holocene) distributions. A database of 901 GPS presence records was generated for the two newts. SDMs were built through Boosted Regression Trees and Maxent, using the Worldclim bioclimatic variables as predictors.ResultsPrecipitation-linked variables and the temperature annual range strongly influence the current occurrence patterns of the two Lissotriton species analyzed. The two newts show opposite responses to the most contributing variables, such as BIO7 (temperature annual range), BIO12 (annual precipitation), BIO17 (precipitation of the driest quarter) and BIO19 (precipitation of the coldest quarter). The hypothesis of climate influencing the distributions of these species is also supported by the fact that the co-occurrences within the sympatric area fall in localities characterized by intermediate values of these predictors. Projections to the Last Glacial Maximum and Holocene scenarios provided a coherent representation of climate influences on the past distributions of the target species. Computation of pairwise variables interactions and the discriminant analysis allowed a deeper interpretation of SDMs’ outputs. Further, we propose a multivariate environmental dissimilarity index (MEDI), derived through a transformation of the multivariate environmental similarity surface (MESS), to deal with extrapolation-linked uncertainties in model projections to past climate. Finally, the niche equivalency and niche similarity tests confirmed the link between SDMs outputs and actual differences in the ecological niches of the two species.ConclusionsThe different responses of the two species to climatic factors have significantly contributed to shape their current distribution, through contractions, expansions and shifts over time, allowing to maintain two wide allopatric areas with an area of sympatry in Central Italy. Moreover, our SDMs hindcasting shows many concordances with previous phylogeographic studies carried out on the same species, thus corroborating the scenarios of potential distribution during the Last Glacial Maximum and the Holocene emerging from the models obtained.

Identifying Reliable Opportunistic Data for Species Distribution Modeling: A Benchmark Data Optimization Approach

The purpose of this study is to increase the number of species occurrence data by integrating opportunistic data with Global Biodiversity Information Facility (GBIF) benchmark data via a novel optimization technique. The optimization method utilizes Natural Language Processing (NLP) and a simulated annealing (SA) algorithm to maximize the average likelihood of species occurrence in maximum entropy presence-only species distribution models (SDM). We applied the Kruskal–Wallis test to assess the differences between the corresponding environmental variables and habitat suitability indices (HSI) among datasets, including data from GBIF, Facebook (FB), and data from optimally selected FB data. To quantify uncertainty in SDM predictions, and to quantify the efficacy of the proposed optimization procedure, we used a bootstrapping approach to generate 1000 subsets from five different datasets: (1) GBIF; (2) FB; (3) GBIF plus FB; (4) GBIF plus optimally selected FB; and (5) GBIF plus randomly selected FB. We compared the performance of simulated species distributions based on each of the above subsets via the area under the curve (AUC) of the receiver operating characteristic (ROC). We also performed correlation analysis between the average benchmark-based SDM outputs and the average dataset-based SDM outputs. Median AUCs of SDMs based on the dataset that combined benchmark GBIF data and optimally selected FB data were generally higher than the AUCs of other datasets, indicating the effectiveness of the optimization procedure. Our results suggest that the proposed approach increases the quality and quantity of data by effectively extracting opportunistic data from large unstructured datasets with respect to benchmark data.

Assessing uncertainty and performance of ensemble conservation planning strategies

Systematic conservation initiatives attempt to cater to the needs of many species via the integration of multiple species distribution models (SDMs), or via the integration of Systematic Conservation Planning (SCP) software, such as Zonation. Unfortunately, due to limited data and knowledge, it is often difficult to select the most suitable model for specific species, let alone an appropriate ensemble modeling method for multiple species. In general, model selection criteria are based on either model performance or consensus. The former integrates the highest-performing SDM for all focal species, whereas the latter integrates multiple SDM outputs based on consensus. While higher-performing ensemble models presumably identify high-quality habitats better, many have argued that high consensus ensemble models have less uncertainty originating from sporadic model variability. This study develops and validates seven ensemble-modeling strategies for integrating outputs of the systematic conservation tool Zonation. First, we considered the distributions of 11 bird species via 100 runs of five SDMs across Taiwan. Second, we evaluated the local and global uncertainty of all five models. Third, we used Zonation to obtain conservation priorities. We then used Principal Component Analysis (PCA) to quantify different sources of uncertainty. Finally, we used independent third-party habitat data to validate each strategy. On average, the ‘best model’ strategy (based on the highest AUC value) performed best. Based on our modeling exercise we present a comprehensive framework for conservation prioritization, validation and the quantification of uncertainty intrinsic to SDMs according to different conservation scenarios and goals.