Profile or group discriminative techniques? Generating reliable species distribution models using pseudo-absences and target-group absences from natural history collections

Preferred prey reduce species realized niche shift and improve range expansion prediction

Many studies have detected realized climatic niche shifts during range changes; this is challenging the fundamental theory of the niche conservatism hypothesis (NCH) and the usefulness of the ecological niche model (ENM) for predicting the distributions of species in space and time by tracking environmental change. Biotic factors such as predatory interactions are important components of species realized niches but are generally difficult to quantify during NCH testing and ENM building. Identifying species' preferred prey may provide a unique opportunity to include trophic interactions in assessing the NCH and determine whether more precise ENM predictions are generated. In this study, we focused on a range-expanding predatory bird, the Asian openbill (Anastomus oscitans). The main prey of the Asian openbill include 136 snail species. We observed a realized climatic niche shift during the northward expansion of the Asian openbill by considering only climates; however, niche conservatism was detected after incorporating their preferred prey. ENMs including preferred snails also predicted the distributions of the Asian openbill better than climate-only models and models including nonpreferred snails or only habitat variables. The results of our study suggested the importance of incorporating preferred prey in evaluating the NCH and developing a framework for predicting the range shifts of both native and alien species in response to global climate change.

Ecological niche models applied to post-megafire vegetation restoration in the context of climate change

Climate change and land-use changes are the main drivers altering fire regimes and leading to the occurrence of megafires. Current management policies mainly focus on short-term restoration without considering how climate change might affect regeneration dynamics. We aimed to test the usefulness of ecological niche models (ENMs) to integrate the effects of climate change on tree species distributions into post-fire restoration planning. We also examined different important conceptual and methodological aspects during this novel process. We constructed ENM at fine spatial resolution (25 m) for the four main tree species (Pinus pinaster, Quercus pyrenaica, Q. faginea and Q. ilex) in an area affected by a megafire in Central Spain at two scales (local and regional), two periods (2 and 14 years after the fire) at the local scale, and under two future climate change scenarios. The usefulness of ENMs as support tools in decision-making for post-fire management was confirmed for the first time. As hypothesized, models developed at both scales are different, since they represent different scale dependent drivers of species distribution patterns. However, both provide objective information to be considered by stakeholders in combination with other sources of information. Local models generated with vegetation data 14 years after the fire provided valuable information about local and current vegetation dynamics (i.e., current microecology spatial niche prediction). Regional models are capable of considering a higher proportion of the climatic niche of species to generate reliable climate change forecasts (i.e., future macroclimate spatial niche forecast). The use of precise ENMs provide both an objective interpretation of potential habitat conditions and the opportunity of examining vegetation patches, that can be very valuable in managing restoration of areas affected by megafires under climate change conditions.

Accounting for niche truncation to improve spatial and temporal predictions of species distributions

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.

Can fisheries bioenergetics modelling refine spatially explicit assessments of climate change vulnerability?

Rising water temperature under climate change is affecting the physiology, population dynamics and geographic distribution of freshwater taxa. We propose a novel application of individual-based bioenergetics modelling (BEM) to assess the physiological impacts of warming on freshwater fishes across broad spatial extents. We test this approach using the Guadalupe bass (Micropterus treculii), a species of conservation and recreational significance that is endemic to central TX, USA. We projected historical-to-future changes (middle 20th century to end of 21st century) in daily bioenergetics of individual fish across 7872 stream reaches and compared this output to changes in reach occupancy derived from traditional species distribution modelling (SDM). SDMs project an 8.7% to 52.1% decrease in reach occupancy, depending on model parameterizations and climate change scenarios. Persistence is projected in the central Edwards Plateau region, whereas extirpations are projected for the warmer southeastern region. BEM projected a median 79.3% and 143.2% increase in somatic growth of age-1 Guadalupe bass across historically occupied reaches under moderate and severe climate change scenarios, respectively. Higher end-of-year body size under future climate was caused by a longer growing season. Future scenarios exploring suppressed or enhanced prey consumption suggest that small changes in prey availability will have relatively greater effects on growth than forecasted changes in temperature. Projected growth was geographically discordant with SDM-based habitat suitability, suggesting that SDMs do not accurately reflect fundamental thermal niche dimensions. Our assessment suggests that for locations where the species persists, Guadalupe bass may benefit from warming, although realized consumption gains will depend on seasonal, spatially varying changes in prey availability and other biotic and abiotic factors. More generally, we demonstrate that uniting species-specific BEM with spatially explicit climate change projections can elucidate the physiological impacts of climate change-including seasonal variation-on freshwater fishes across broad geographic extents to complement traditional SDM.

Feature tuning improves MAXENT predictions of the potential distribution of Pedicularis longiflora Rudolph and its variant

Pedicularis longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) are alpine plants and traditional Chinese medicines with important medicinal value, and future climate changes may have an adverse impact on their geographic distribution. The maximum entropy (MAXENT) model has the outstanding ability to predict the potential distribution region of species under climate change. Therefore, given the importance of the parameter settings of feature classes (FCs) and the regularization multiplier (RM) of the MAXENT model and the importance of add indicators to evaluate model performance, we used ENMeval to improve the MAXENT niche model and conducted an in-depth study on the potential distributions of these two alpine medicinal plants. We adjusted the parameters of FC and RM in the MAXENT model, evaluated the adjusted MAXENT model using six indicators, determined the most important ecogeographical factors (EGFs) that affect the potential distributions of these plants, and compared their current potential distributions between the adjusted model and the default model. The adjusted model performed better; thus, we used the improved MAXENT model to predict their future potential distributions. The model predicted that P. longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) would move northward and showed a decrease in extent under future climate scenarios. This result is important to predict their potential distribution regions under changing climate scenarios to develop effective long-term resource conservation and management plans for these species.

Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

The combined effects of climate change and river fragmentation on the distribution of Andean Amazon fishes

Upstream range shifts of freshwater fishes have been documented in recent years due to ongoing climate change. River fragmentation by dams, presenting physical barriers, can limit the climatically induced spatial redistribution of fishes. Andean freshwater ecosystems in the Neotropical region are expected to be highly affected by these future disturbances. However, proper evaluations are still missing. Combining species distribution models and functional traits of Andean Amazon fishes, coupled with dam locations and climatic projections (2070s), we (a) evaluated the potential impacts of future climate on species ranges, (b) investigated the combined impact of river fragmentation and climate change and (c) tested the relationships between these impacts and species functional traits. Results show that climate change will induce range contraction for most of the Andean Amazon fish species, particularly those inhabiting highlands. Dams are not predicted to greatly limit future range shifts for most species (i.e., the Barrier effect). However, some of these barriers should prevent upstream shifts for a considerable number of species, reducing future potential diversity in some basins. River fragmentation is predicted to act jointly with climate change in promoting a considerable decrease in the probability of species to persist in the long-term because of splitting species ranges in smaller fragments (i.e., the Isolation effect). Benthic and fast-flowing water adapted species with hydrodynamic bodies are significantly associated with severe range contractions from climate change.

Multi-scale habitat modelling and predicting change in the distribution of tiger and leopard using random forest algorithm

Tigers and leopards have experienced considerable declines in their population due to habitat loss and fragmentation across their historical ranges. Multi-scale habitat suitability models (HSM) can inform forest managers to aim their conservation efforts at increasing the suitable habitat for tigers by providing information regarding the scale-dependent habitat-species relationships. However the current gap of knowledge about ecological relationships driving species distribution reduces the applicability of traditional and classical statistical approaches such as generalized linear models (GLMs), or occupancy surveys to produce accurate predictive maps. This study investigates the multi-scale habitat relationships of tigers and leopards and the impacts of future climate change on their distribution using a machine-learning algorithm random forest (RF). The recent advancements in the machine-learning algorithms provide a powerful tool for building accurate predictive models of species distribution and their habitat relationships even when little ecological knowledge is available about the species. We collected species occurrence data using camera traps and indirect evidence of animal presences (scats) in the field over 2 years of rigorous sampling and used a machine-learning algorithm random forest (RF) to predict the habitat suitability maps of tiger and leopard under current and future climatic scenarios. We developed niche overlap models based on the recently developed statistical approaches to assess the patterns of niche similarity between tigers and leopards. Tiger and leopard utilized habitat resources at the broadest spatial scales (28,000 m). Our model predicted a 23% loss in the suitable habitat of tigers under the RCP 8.5 Scenario (2050). Our study of multi-scale habitat suitability modeling provides valuable information on the species habitat relationships in disturbed and human-dominated landscapes concerning two large felid species of conservation importance. These areas may act as refugee habitats for large carnivores in the future and thus should be the focus of conservation importance. This study may also provide a methodological framework for similar multi-scale and multi-species monitoring programs using robust and more accurate machine learning algorithms such as random forest.

Mapping tree species vulnerability to multiple threats as a guide to restoration and conservation of tropical dry forests

Understanding the vulnerability of tree species to anthropogenic threats is important for the efficient planning of restoration and conservation efforts. We quantified and compared the effects of future climate change and four current threats (fire, habitat conversion, overgrazing and overexploitation) on the 50 most common tree species of the tropical dry forests of northwestern Peru and southern Ecuador. We used an ensemble modelling approach to predict species distribution ranges, employed freely accessible spatial datasets to map threat exposures, and developed a trait-based scoring approach to estimate species-specific sensitivities, using differentiated trait weights in accordance with their expected importance in determining species sensitivities to specific threats. Species-specific vulnerability maps were constructed from the product of the exposure maps and the sensitivity estimates. We found that all 50 species face considerable threats, with an average of 46% of species' distribution ranges displaying high or very high vulnerability to at least one of the five threats. Our results suggest that current levels of habitat conversion, overexploitation and overgrazing pose larger threats to most of the studied species than climate change. We present a spatially explicit planning strategy for species-specific restoration and conservation actions, proposing management interventions to focus on (a) in situ conservation of tree populations and seed collection for tree planting activities in areas with low vulnerability to climate change and current threats; (b) ex situ conservation or translocation of populations in areas with high climate change vulnerability; and (c) active planting or assisted regeneration in areas under high current threat vulnerability but low climate change vulnerability, provided that interventions are in place to lower threat pressure. We provide an online, user-friendly tool to visualize both the vulnerability maps and the maps indicating priority restoration and conservation actions.

Urban Niche Assessment: An Approach Integrating Social Media Analysis, Spatial Urban Indicators and Geo-Statistical Techniques

Cities are human ecosystems. Understanding human ecology is important for designing and planning the built environment. The ability to respond to changes and adapt actions in a positive way helps determine the health of cities. Recently, many studies have highlighted the great potential of photographic data shared on the Flickr platform for the analysis of environmental perceptions in landscape and urban planning. Other research works used panoramic images from the Google Street View (GSV) web service to extract urban quality data. Although other researches have used social media to characterize human habitat from an emotional point of view, there is still a lack of knowledge of the correlation between environmental and physical variables of the city and visual perception, especially at a scale suitable for urban planning and design. In ecology, the environmental suitability of a territory for a given biological community is studied through species distribution models (SDM). In this work we have adopted the state of the art of SDM (the ensemble approach) to develop a methodology transferable to cities with different sizes and characteristics that uses data deriving from many sources available on a global scale: social media platform, Google internet services, shared geographical information, remote sensing and geomorphological data. The result of our application in the city of Livorno offers important information on the most significant variables for the conservation, planning and design of urban public spaces at the project scale. However, further research developments will be needed to test the model in cities of different sizes and geographic locations, integrate the model with other social media, other databases and with traditional surveys and improve the quality of indicators that can be derived from information shared on the Internet.

Climatic Niche Shift during Azolla filiculoides Invasion and Its Potential Distribution under Future Scenarios

In order to prevent future biological invasions, it is crucial to know non-native species distributions. We evaluated the potential global distribution of Azolla filiculoides, a free-floating macrophyte native to the Americas by using species distribution models and niche equivalency tests to analyze the degree of niche overlap between the native and invaded ranges of the species. The models were projected under two future emission scenarios, three global circulation models and two time periods. Our results indicate a possible niche shift between the distribution ranges of the species, indicating that A. filiculoides can adapt to novel environmental conditions derived from climatic differences during the invasion process. Our models also show that the future potential distribution of A. filiculoides will decrease globally, although the species could colonize new vulnerable regions where it is currently absent. We highlight that species occurrence records in the invaded area are necessary to generate accurate models, which will, in turn, improve our ability to predict potential invasion risk areas.

The use of classification and regression algorithms using the random forests method with presence-only data to model species' distribution

Random forests (RF) is a powerful species distribution model (SDM) algorithm. This ensemble model by default can produce categorical and numerical species distribution maps based on its classification tree (CT) and regression tree (RT) algorithms, respectively. The CT algorithm can also produce numerical predictions (class probability). Here, we present a detailed procedure involving the use of the CT and RT algorithms using the RF method with presence-only data to model the distribution of species. CT and RT are used to generate numerical prediction maps, and then numerical predictions are converted to binary predictions through objective threshold-setting methods. We also applied simple methods to deal with collinearity of predictor variables and spatial autocorrelation of species occurrence data. A geographically stratified sampling method was employed for generating pseudo-absences. The detailed procedural framework is meant to be a generic method to be applied to virtually any SDM prediction question using presence-only data.

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How to use RF as a standard method for generic species distributions with presence-only data

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How to choose RF (CT or RT) methods for the distribution modeling of species

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A general and detailed procedure for any SDM prediction question.

Current and future climatic regions favourable for a globally introduced wild carnivore, the raccoon Procyon lotor

Invasive species are considered as one of the major threats to biodiversity and represent a major challenge in the conservation of natural ecosystems, in preventing damage to agricultural production, and human health risks. Environmental Niche Modelling has emerged as a powerful tool to predict the patterns of range expansion of non-native species and to direct effective strategies for managing biological invasions. The raccoon, Procyon lotor, is a wild mesocarnivore presenting a high adaptability and showing successful introduced populations worldwide. Here, we modelled the current and future climatically favourable areas for the raccoon using two protocols, based on data sets filtrated in geographic and environmental spaces. Projections from these models show extensive current favourable geographical areas covering extensive regions of temperate biomes. Moreover, predictions for 2050 reveals extensive new favourable areas north of the current favourable regions. However, the results of the two modeling approaches differ in the extent of predicted favourable spaces. Protocols using geographically filtered data present more conservative forecasts, while protocol using environmental filtration presents forecasts across greater areas. Given the biological characteristics and the ecological requirements of a generalist carnivore such as the raccoon, the latter forecasts appears more relevant and should be privileged in the development of conservation plans for ecosystems.

Ecological niche modeling predicting the potential distribution of Leishmania vectors in the Mediterranean basin: impact of climate change

Background

Due to climate change, the geographical distribution of sand flies during the last decades has shifted northward from latitudes below 45°N in southern Europe to latitudes just above 50^○N. Recent studies show that some phlebotomine sand flies were recorded in several parts of Germany and Belgium. In central Europe, some autochthone leishmaniasis cases are being recorded in regions traditionally regarded as leishmaniasis-free. An important challenge is to predict the geographical distribution of leishmaniasis vectors under new climatic conditions. In this study, we attempted to predict the current distribution of six leishmaniasis vectors in the Mediterranean basin and forecast species’ geographical shift under future climate scenarios using an ensemble ecological niche modeling approach. Species records were obtained from scientific surveys published in the research literature between 2006 and 2016. A series of climate metrics describing temperature and precipitation in the study area under two climatic scenarios were obtained from WorldClim database. A consensus model was derived from six varieties of modeling approaches (regression, machine learning and classification techniques) in order to ensure valid prediction of distribution of vectors under different climate scenarios.

Results

Model performance was generally high for the included species with a specificity (true negative rate) ranging from 81.03 to 96.52% (mean = 86.94%) and a sensitivity (true positive rate) ranging from 87.93 to 100% (mean = 96.98%). Our work evidenced the hypothesis of the widespread of Leishmania vectors under climate change scenarios. All of the studied species are prospected to gain new areas that are actually not suitable for vectors’ survival. Phlebotomine sand flies are prospected to invade extra-Mediterranean regions, especially western and central Europe.

Conclusions

Our study confirmed the importance of environmental and climate factors on the distribution of leishmaniasis vectors and demonstrated the performance of ecological niche modeling in the prediction of the geographical spread of vector-borne diseases. Ecological niche modeling should be considered in the future as a valuable tool in addition to experimental laboratory studies for a better understanding of the biology of vector species.

A climate-associated multispecies cryptic cline in the northwest Atlantic

The spatial genetic structure of most species in the open marine environment remains largely unresolved. This information gap creates uncertainty in the sustainable management, recovery, and associated resilience of marine communities and our capacity to extrapolate beyond the few species for which such information exists. We document a previously unidentified multispecies biogeographic break aligned with a steep climatic gradient and driven by seasonal temperature minima in the northwest Atlantic. The coherence of this genetic break across our five study species with contrasting life histories suggests a pervasive macroecological phenomenon. The integration of this genetic structure with habitat suitability models and climate forecasts predicts significant variation in northward distributional shifts among populations and availability of suitable habitat in future oceans. The results of our integrated approach provide new perspective on how cryptic intraspecific diversity associated with climatic variation influences species and community response to climate change beyond simple poleward shifts.

The interplay of climate and land use change affects the distribution of EU bumblebees

Bumblebees in Europe have been in steady decline since the 1900s. This decline is expected to continue with climate change as the main driver. However, at the local scale, land use and land cover (LULC) change strongly affects the occurrence of bumblebees. At present, LULC change is rarely included in models of future distributions of species. This study's objective is to compare the roles of dynamic LULC change and climate change on the projected distribution patterns of 48 European bumblebee species for three change scenarios until 2100 at the scales of Europe, and Belgium, Netherlands and Luxembourg (BENELUX). We compared three types of models: (1) only climate covariates, (2) climate and static LULC covariates and (3) climate and dynamic LULC covariates. The climate and LULC change scenarios used in the models include, extreme growth applied strategy (GRAS), business as might be usual and sustainable European development goals. We analysed model performance, range gain/loss and the shift in range limits for all bumblebees. Overall, model performance improved with the introduction of LULC covariates. Dynamic models projected less range loss and gain than climate-only projections, and greater range loss and gain than static models. Overall, there is considerable variation in species responses and effects were most pronounced at the BENELUX scale. The majority of species were predicted to lose considerable range, particularly under the extreme growth scenario (GRAS; overall mean: 64% ± 34). Model simulations project a number of local extinctions and considerable range loss at the BENELUX scale (overall mean: 56% ± 39). Therefore, we recommend species-specific modelling to understand how LULC and climate interact in future modelling. The efficacy of dynamic LULC change should improve with higher thematic and spatial resolution. Nevertheless, current broad scale representations of change in major land use classes impact modelled future distribution patterns.

Past, current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands, Ecuador

In order to enhance in terms of accuracy and predict the modeling of the potential distribution of species, the integration of using principal components of environmental variables as input of maximum entropy (MaxEnt) has been proposed in this study. Principal components selected previously from the principal component analysis results performed in ArcGIS in the environmental variables was used as an input data of MaxEnt instead of raw data to model the potential distribution of red spiny lobster from the year 1997 to 2015 and for three different future scenarios 2020, 2050, and 2070. One set of six original environmental variables pertaining to the years 1997–2015 and one set of four variables for future scenarios were transformed independently into a single multiband raster in ArcGIS in order to select the variables whose eigenvalues explains more than 5% of the total variance with the purpose to use in the modeling prediction in MaxEnt. The years 1997 and 1998 were chosen to compare the accuracy of the model, showing better results using principal components instead of raw data in terms of area under the curve and partial receiver operating characteristic as well as better predictions of suitable areas. Using principal components as input of MaxEnt enhances the prediction of good habitat suitability for red spiny lobster; however, future scenarios suggest an adequate management by researches to elaborate appropriate guidelines for the conservation of the habitat for this valuable specie with face to the climate change.

How to describe species richness patterns for bryophyte conservation?

A large amount of data for inconspicuous taxa is stored in natural history collections; however, this information is often neglected for biodiversity patterns studies. Here, we evaluate the performance of direct interpolation of museum collections data, equivalent to the traditional approach used in bryophyte conservation planning, and stacked species distribution models (S‐SDMs) to produce reliable reconstructions of species richness patterns, given that differences between these methods have been insufficiently evaluated for inconspicuous taxa. Our objective was to contrast if species distribution models produce better inferences of diversity richness than simply selecting areas with the higher species numbers. As model species, we selected Iberian species of the genus Grimmia (Bryophyta), and we used four well‐collected areas to compare and validate the following models: 1) four Maxent richness models, each generated without the data from one of the four areas, and a reference model created using all of the data and 2) four richness models obtained through direct spatial interpolation, each generated without the data from one area, and a reference model created with all of the data. The correlations between the partial and reference Maxent models were higher in all cases (0.45 to 0.99), whereas the correlations between the spatial interpolation models were negative and weak (−0.3 to −0.06). Our results demonstrate for the first time that S‐SDMs offer a useful tool for identifying detailed richness patterns for inconspicuous taxa such as bryophytes and improving incomplete distributions by assessing the potential richness of under‐surveyed areas, filling major gaps in the available data. In addition, the proposed strategy would enhance the value of the vast number of specimens housed in biological collections.

Predicting potential distribution of poorly known species with small database: the case of four-horned antelope Tetracerus quadricornis on the Indian subcontinent

Information gaps on the distribution of data deficient and rare species such as four-horned antelope (FHA) in Nepal may impair their conservation. We aimed to empirically predict the distribution of FHA in Nepal with the help of data from the Indian subcontinent. Additionally, we wanted to identify core areas and gaps within the reported range limits and to assess the degree of isolation of known Nepalese populations from the main distribution areas in India. The tropical part of the Indian subcontinent (65°-90° eastern longitude, 5°-30° northern latitude), that is, the areas south of the Himalayan Mountains. Using MaxEnt and accounting for sampling bias, we developed predictive distribution models from environmental and topographical variables, and known presence locations of the study species in India and Nepal. We address and discuss the use of target group vs. random background. The prediction map reveals a disjunct distribution of FHA with core areas in the tropical parts of central to southern-western India. At the scale of the Indian subcontinent, suitable FHA habitat area in Nepal was small. The Indo-Gangetic Plain isolates Nepalese from the Indian FHA populations, but the distribution area extends further south than proposed by the current IUCN map. A low to intermediate temperature seasonality as well as low precipitation during the dry and warm season contributed most to the prediction of FHA distribution. The predicted distribution maps confirm other FHA range maps but also indicate that suitable areas exist south of the known range. Results further highlight that small populations in the Nepalese Terai Arc are isolated from the Indian core distribution and therefore might be under high extinction risk.

Worldwide Alien Invasion: A Methodological Approach to Forecast the Potential Spread of a Highly Invasive Pollinator

The ecological impacts of alien species invasion are a major threat to global biodiversity. The increasing number of invasion events by alien species and the high cost and difficulty of eradicating invasive species once established require the development of new methods and tools for predicting the most susceptible areas to invasion. Invasive pollinators pose serious threats to biodiversity and human activity due to their close relationship with many plants (including crop species) and high potential competitiveness for resources with native pollinators. Although at an early stage of expansion, the bumblebee species Bombus terrestris is becoming a representative case of pollinator invasion at a global scale, particularly given its high velocity of invasive spread and the increasing number of reports of its impacts on native bees and crops in many countries. We present here a methodological framework of habitat suitability modeling that integrates new approaches for detecting habitats that are susceptible to Bombus terrestris invasion at a global scale. Our approach did not include reported invaded locations in the modeling procedure; instead, those locations were used exclusively to evaluate the accuracy of the models in predicting suitability over regions already invaded. Moreover, a new and more intuitive approach was developed to select the models and evaluate different algorithms based on their performance and predictive convergence. Finally, we present a comprehensive global map of susceptibility to Bombus terrestris invasion that highlights priority areas for monitoring.

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.

Modeling of spatial distribution for scorpions of medical importance in the São Paulo State, Brazil

Aim:

In this work, we aimed to develop maps of modeling geographic distribution correlating to environmental suitability for the two species of scorpions of medical importance at São Paulo State and to develop spatial configuration parameters for epidemiological surveillance of these species of venomous animals.

Materials and Methods:

In this study, 54 georeferenced points for Tityus serrulatus and 86 points for Tityus bahiensis and eight environmental indicators, were used to generate species distribution models in Maxent (maximum entropy modeling of species geographic distributions) version 3.3.3k using 70% of data for training (n=38 to T. serrulatus and n=60 to T. bahiensis) and 30% to test the models (n=16 for T. serrulatus and n=26 for T. bahiensis). The logistic threshold used to cut models in converting the continuous probability model into a binary model was the “maximum test sensitivity plus specificity,” provided by Maxent, with results of 0.4143 to T. serrulatus and of 0.3401 to T. bahiensis. The models were evaluated by the area under the curve (AUC), using the omission error and the binomial probability. With the data generated by Maxent, distribution maps were produced using the “ESRI^® ArcGIS 10.2.2 for Desktop” software.

Results:

The models had high predictive success (AUC=0.7698±0.0533, omission error=0.2467 and p<0.001 for T. serrulatus and AUC=0.8205±0.0390, omission error=0.1917 and p<0.001 for T. bahiensis) and the resultant maps showed a high environmental suitability in the north, central, and southeast of the state, confirming the increasing spread of these species. The environmental variables that mostly contributed to the scorpions species distribution model were rain precipitation (28.9%) and tree cover (28.2%) for the T. serrulatus and temperature (45.8%) and thermal amplitude (12.6%) for the T. bahiensis.

Conclusion:

The distribution model of these species of medical importance scorpions in São Paulo State revealed a higher environmental suitability of these species in the regions north, central, and southeast of the state, warning to emergencies actions for prevention and surveillance from scorpion stings in several counties. There is also a need to best conservation strategies related to neighboring territories, with the implementation of new environmental protected areas and measures of spread control of these species in urban areas of several counties.

Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.

Population signatures of large-scale, long-term disjunction and small-scale, short-term habitat fragmentation in an Afromontane forest bird

The Eastern Afromontane cloud forests occur as geographically distinct mountain exclaves. The conditions of these forests range from large to small and from fairly intact to strongly degraded. For this study, we sampled individuals of the forest bird species, the Montane White-eye Zosterops poliogaster from 16 sites and four mountain archipelagos. We analysed 12 polymorphic microsatellites and three phenotypic traits, and calculated Species Distribution Models (SDMs) to project past distributions and predict potential future range shifts under a scenario of climate warming. We found well-supported genetic and morphologic clusters corresponding to the mountain ranges where populations were sampled, with 43% of all alleles being restricted to single mountains. Our data suggest that large-scale and long-term geographic isolation on mountain islands caused genetically and morphologically distinct population clusters in Z. poliogaster. However, major genetic and biometric splits were not correlated to the geographic distances among populations. This heterogeneous pattern can be explained by past climatic shifts, as highlighted by our SDM projections. Anthropogenically fragmented populations showed lower genetic diversity and a lower mean body mass, possibly in response to suboptimal habitat conditions. On the basis of these findings and the results from our SDM analysis we predict further loss of genotypic and phenotypic uniqueness in the wake of climate change, due to the contraction of the species' climatic niche and subsequent decline in population size.

Diversity and distribution of deep-sea shrimps in the Ross Sea region of Antarctica

Although decapod crustaceans are widespread in the oceans, only Natantia (shrimps) are common in the Antarctic. Because remoteness, depth and ice cover restrict sampling in the South Ocean, species distribution modelling is a useful tool for evaluating distributions. We used physical specimen and towed camera data to describe the diversity and distribution of shrimps in the Ross Sea region of Antarctica. Eight shrimp species were recorded: Chorismus antarcticus; Notocrangon antarcticus; Nematocarcinus lanceopes; Dendrobranchiata; Pasiphaea scotiae; Pasiphaea cf. ledoyeri; Petalidium sp., and a new species of Lebbeus. For the two most common species, N. antarcticus and N. lanceopes, we used maximum entropy modelling, based on records of 60 specimens and over 1130 observations across 23 sites in depths from 269 m to 3433 m, to predict distributions in relation to environmental variables. Two independent sets of environmental data layers at 0.05° and 0.5° resolution respectively, showed how spatial resolution affected the model. Chorismus antarcticus and N. antarcticus were found only on the continental shelf and upper slopes, while N. lanceopes, Lebbeus n. sp., Dendrobranchiata, Petalidium sp., Pasiphaea cf. ledoyeri, and Pasiphaea scotiae were found on the slopes, seamounts and abyssal plain. The environmental variables that contributed most to models for N. antarcticus were depth, chlorophyll-a concentration, temperature, and salinity, and for N. lanceopes were depth, ice concentration, seabed slope/rugosity, and temperature. The relative ranking, but not the composition of these variables changed in models using different spatial resolutions, and the predicted extent of suitable habitat was smaller in models using the finer-scale environmental layers. Our modelling indicated that shrimps were widespread throughout the Ross Sea region and were thus likely to play important functional role in the ecosystem, and that the spatial resolution of data needs to be considered both in the use of species distribution models.

Climate niche modeling in the perennial Glycine (Leguminosae) allopolyploid complex

PREMISE OF STUDY

Polyploid plants, when compared with diploids, show similar molecular, morphological, physiological, and ecological tendencies across unrelated groups, but the degree to which these form "rules" of polyploid evolution are unclear. The Glycine (Leguminosae) allopolyploid complex affords the opportunity to test whether polyploidy in similar genetic backgrounds produces similar effects on geographical range or climatic space.

METHODS

We used information on locality presence of four closely related Glycine allopolyploid species and their diploid progenitors to build models of the potentially available Australian ranges based on climate using Maxent3.3.3k. Principal coordinate analysis was used to characterize the multidimensional climate space occupied by each species.

KEY RESULTS

Each of the four Glycine allopolyploids showed intermediacy in potential geographical space and in ecological space, relative to its diploid progenitors. The four allopolyploids did not have consistently larger ranges than their progenitors, though all four occupied a portion of climate niche space not available to its progenitors. The polyploids also differed in their exploitation of potentially available geographical range. Australian ranges and environmental space did not correlate with greater colonizing ability in these polyploids.

CONCLUSIONS

The four Glycine allopolyploids do not show many common range- or climate-related features, other than intermediacy. Thus, despite their similar genetic and evolutionary backgrounds, polyploidy has not produced convergent ecological effects.

Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions.

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Substitutable habitats? The biophysical and anthropogenic drivers of an exotic bird’s distribution

The spread and distribution of exotic species depends on a number of factors, both anthropogenic and biophysical. The importance of each factor may vary geographically, making it difficult to predict where a species will spread. In this paper, we examine the factors that influence the distribution of monk parakeets (Myiopsitta monachus), a parrot native to South America that has become established in the United States. We use monk parakeet observations gathered from citizen-science datasets to inform a series of random forest models that examine the relative importance of biophysical and anthropogenic variables in different regions of the United States. We find that while the distribution of monk parakeets in the southern US is best explained by biophysical variables such as January dew point temperature and forest cover, the distribution of monk parakeets in the northern US appears to be limited to urban environments. Our results suggest that monk parakeets are unlikely to spread outside of urban environments in the northern United States, as they are not adapted to the climatic conditions in that region. We extend the notion of “substitutable habitats,” previously applied to different habitats in the same landscape, to exotic species in novel landscapes (e.g., cities). These novel landscapes provide resources and environmental conditions that, although very different from the species’ native habitat, still enable them to become established. Our results highlight the importance of understanding the regionally-specific factors that allow an exotic species to become established, which is key to predicting their expansion beyond areas of introduction.

Climate, vegetation, introduced hosts and trade shape a global wildlife pandemic

Global factors, such as climate change, international trade and introductions of exotic species are often elicited as contributors to the unprecedented rate of disease emergence, but few studies have partitioned these factors for global pandemics. Although contemporary correlative species distribution models (SDMs) can be useful for predicting the spatial patterns of emerging diseases, they focus mainly on the fundamental niche (FN) predictors (i.e. abiotic climate and habitat factors), neglecting dispersal and propagule pressure predictors (PP, number of non-native individuals released into a region). Using a validated, predictive and global SDM, we show that both FN and PP accounted for significant, unique variation to the distribution of the chytrid fungus Batrachochytrium dendrobatidis (Bd), a pathogen implicated in the declines and extinctions of over 200 amphibian species worldwide. Bd was associated positively with vegetation, total trade and introduced amphibian hosts, nonlinearly with annual temperature range and non-significantly with amphibian leg trade or amphibian species richness. These findings provide a rare example where both FN and PP factors are predictive of a global pandemic. Our model should help guide management of this deadly pathogen and the development of other globally predictive models for species invasions and pathogen emergence influenced by FN and PP factors.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

The impact of climate change on indigenous Arabica coffee (Coffea arabica): predicting future trends and identifying priorities

Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020-2050), representing assessment priorities for ex situ conservation; (2) identifies 'core localities' that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing plantations will be neagtively impacted by climate change.