Challenges in identifying sites climatically matched to the native ranges of animal invaders

Background

Species distribution models are often used to characterize a species'' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt''s utility for vertebrate climate matching.

Methodology/Principal Findings

Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.''s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike''s Information Criterion.

Conclusions/Significance

When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.     

Climatic niche of the Saker Falcon Falco cherrug: predicted new areas to direct population surveys in Central Asia

Accurate species distribution data across remote and extensive geographical areas are difficult to obtain. Here, we use bioclimatic envelope models to determine climatic constraints on the distribution of the migratory Saker Falcon Falco cherrug to identify areas in data-deficient regions that may contain unidentified populations. Sakers live at low densities across large ranges in remote regions, making distribution status difficult to assess. Using presence-background data and eight bioclimatic variables within a species distribution modelling framework, we applied MaxEnt to construct models for both breeding and wintering ranges. Occurrence data were spatially filtered and climatic variables tested for multicollinearity before selecting best fit models using the Akaike information criterion by tuning MaxEnt parameters. Model predictive performance tested using the continuous Boyce index (B) was high for both breeding (B_TEST = 0.921) and wintering models (B_TEST = 0.735), with low omission rates and minimal overfitting. The Saker climatic niche was defined by precipitation in the warmest quarter in the breeding range model, and mean temperature in the wettest quarter in the wintering range model. Our models accurately predicted areas of highest climate suitability and defined the climatic constraints on a wide-ranging rare species, suggesting that climate is a key determinant of Saker distribution across macro-scales. We recommend targeted population surveys for the Saker based on model predictions to areas of highest climatic suitability in key regions with distribution knowledge gaps, in particular the Qinghai-Tibet plateau in western China. Further applications of our models could identify protected areas and reintroduction sites, inform development conflicts, and assess the impact of climate change on distributions.     

Detecting outliers in species distribution data

Aim

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

Location

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

Methods

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

Results

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

Main conclusions

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

MaxEnt模型参数设置对其所模拟物种地理分布和生态位的影响——以茶翅蝽为例

【目的】生态位模型被广泛应用于入侵生物学和保护生物学研究,现有建模工具中,MaxEnt是最流行和运用最广泛的生态位模型。然而最近研究表明,基于MaxEnt模型的默认参数构建模型时,模型倾向于过度拟合,并非一定为最佳模型,尤其是在处理一些分布点较少的物种。【方法】以茶翅蝽为例,通过设置不同的特征参数、调控倍频以及背景拟不存在点数分别构建茶翅蝽的本土模型,然后将其转入入侵地来验证和比较模型,通过检测模型预测的物种对环境因子的响应曲线、潜在分布在生态空间中的生态位映射以及潜在分布的空间差异性,探讨3种参数设置对MaxEnt模型模拟物种分布和生态位的影响。【结果】在茶翅蝽的案例分析中,特征参数的设置对MaxEnt模型所模拟的潜在分布和生态位的影响最大,调控倍频的影响次之,背景拟不存在点数的影响最小。与其他特征相比,基于特征H和T的模型其响应曲线较为曲折;随着调控倍频的增加,响应曲线变得圆滑。【结论】在构建MaxEnt模型时,需要从生态空间中考虑物种的生态需求,分析模型参数对预测物种分布和生态位可能造成的影响。     

Modelling invasive alien species distributions from digital biodiversity atlases. Model upscaling as a means of reconciling data at different scales

Aim

There is a wealth of information on species occurrences in biodiversity data banks, albeit presence‐only, biased and scarce at fine resolutions. Moreover, fine‐resolution species maps are required in biodiversity conservation. New techniques for dealing with this kind of data have been reported to perform well. These fine‐resolution maps would be more robust if they could explain data at coarser resolutions at which species distributions are well represented. We present a new methodology for testing this hypothesis and apply it to invasive alien species (IAS).

Location

Catalonia, Spain.

Methods

We used species presence records from the Biodiversity data bank of Catalonia to model the distribution of ten IAS which, according to some recent studies, achieve their maximum distribution in the study area. To overcome problems inherent with the data, we prepared different correction treatments: three for dealing with bias and five for autocorrelation. We used the MaxEnt algorithm to generate models at 1‐km resolution for each species and treatment. Acceptable models were upscaled to 10 km and validated against independent 10 km occurrence data.

Results

Of a total of 150 models, 20 gave acceptable results at 1‐km resolution and 12 passed the cross‐scale validation test. No apparent pattern emerged, which could serve as a guide on modelling. Only four species gave models that also explained the distribution at the coarser scale.

Main conclusions

Although some techniques may apparently deliver good distribution maps for species with scarce and biased data, they need to be taken with caution. When good independent data at a coarser scale are available, cross‐scale validation can help to produce more reliable and robust maps. When no independent data are available for validation, however, new data gathering field surveys may be the only option if reliable fine‐scale resolution maps are needed.     

Coexistence of insect species competing for a pulsed resource: toward a unified theory of biodiversity in fluctuating environments

Background

One major challenge in understanding how biodiversity is organized is finding out whether communities of competing species are shaped exclusively by species-level differences in ecological traits (niche theory), exclusively by random processes (neutral theory of biodiversity), or by both processes simultaneously. Communities of species competing for a pulsed resource are a suitable system for testing these theories: due to marked fluctuations in resource availability, the theories yield very different predictions about the timing of resource use and the synchronization of the population dynamics between the competing species. Accordingly, we explored mechanisms that might promote the local coexistence of phytophagous insects (four sister species of the genus Curculio) competing for oak acorns, a pulsed resource.

Methodology/Principal Findings

We analyzed the time partitioning of the exploitation of oak acorns by the four weevil species in two independent communities, and we assessed the level of synchronization in their population dynamics. In accordance with the niche theory, overall these species exhibited marked time partitioning of resource use, both within a given year and between different years owing to different dormancy strategies between species, as well as distinct demographic patterns. Two of the four weevil species, however, consistently exploited the resource during the same period of the year, exhibited a similar dormancy pattern, and did not show any significant difference in their population dynamics.

Conclusions/Significance

The marked time partitioning of the resource use appears as a keystone of the coexistence of these competing insect species, except for two of them which are demographically nearly equivalent. Communities of consumers of pulsed resources thus seem to offer a promising avenue for developing a unifying theory of biodiversity in fluctuating environments which might predict the co-occurrence, within the same community, of species that are ecologically either very similar, or very different.     

基于MaxEnt模型的云南干热河谷适生树种选择

基于MaxEnt模型识别和预测云南干热河谷适生树种,对于改善和恢复其生态治理能力具有重要意义。收集40种具有代表性的潜在适生树种地理分布数据,结合气候、地形和土壤等环境因子,利用MaxEnt模型筛选适生树种。预测当前和2021-2040年四种气候情景下(SSP126、SSP245、SSP370和SSP585)适生树种适生区的分布格局,划分优先种植区域,并明确MaxEnt模型用于树种选择的可行性。结果表明:(1)当前气候情景下,影响干热河谷潜在适生树种分布的主导因子是气候因子,其次是海拔、植物归一化指数、地表太阳辐射量和人类足迹。(2)未来,24种适生树种适生区稳定,发生概率与海拔关系呈单峰分布且高海拔下适生树种丰富度将降低。(3)干热河谷适生树种优先种植区域沿干热河谷呈狭长分布;实际调查发现,元谋县适生树种实际分布区域面积略高于最佳种植区域面积。应用MaxEnt模型筛选干热河谷适生树种选择是可行的,但在应用之前必须通过实地调查来验证树种实际生存情况与预测结果的差异。在干热河谷生态修复造林时,可优先考虑白枪杆、车桑子等24种树种。     

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

Aim

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

Location

The shallow seas.

Methods

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

Results

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

Main conclusions

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

     

Depth and temperature drive patterns of spatial overlap among fish thermal guilds in lakes across Ontario,Canada

Aim

The spatial distribution of ectotherms is strongly dependent on the temperature of their environments. In temperate lakes, fishes with different thermal optima can become spatially segregated during summer stratification. This habitat partitioning, or niche complementarity, may play a role in the coexistence of trophically similar species; however, the extent of partitioning is dependent on the resources available within each habitat. Although habitat partitioning of fish thermal guilds has been studied in individual lakes, broad-scale patterns of spatial overlap and segregation are not yet understood. In this study, we explore the patterns and drivers of spatial overlap among thermal guilds (cold-, cool-, and warm-water) at a broad scale.

Location

Ontario, Canada.

Methods

We built a multivariate regression tree to explore patterns and environmental drivers of spatial overlap in freshwater fishes across three thermal guilds from 438 lakes.

Results

We identified five clusters of lakes exhibiting different patterns of spatial overlap among the three thermal guilds. Temperature (growing degree days) and maximum lake depth were strong drivers of the spatial overlap patterns.

Main Conclusions

These findings provide a better understanding of broad-scale patterns of spatial overlap and allow us to predict how spatial overlap, and ultimately species interactions and competition, may change under a warming climate.     

Modeling individual tree height to diameter ratio for Norway spruce and European beech in Czech Republic

Key message

The purposed spatially explicit and spatially non-explicit height to diameter ratio models can be useful to evaluate the stability of trees and stands for Norway spruce and European beech forests.

Abstract

Height to diameter ratio (HDR) is an individual tree index, also known as slenderness coefficient, and commonly used to evaluate stability of trees and stands. We developed both spatially explicit and spatially non-explicit HDR models for Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.) using a large dataset collected from fully stem-mapped permanent research plots in various parts of the Czech Republic. Various tree and stand characteristics were evaluated for their potential contributions to the the HDR models. In addition to diameter at breast height (DBH), other highly significant predictor variables identified are dominant height (HDOM) (site quality measure), dominant diameter (DDOM) and quadratic mean diameter (QMD) (spatially non-explicit competition measures), and Hegyi’s index (spatially explicit competition index, CI). A simple exponential decay function was chosen as a base function to include these predictor variables. Both spatially explicit and spatially non-explicit models described large parts of the HDR variations [R _adj ²  = 0.66 (Norway spruce), 0.72 (European beech)] without any systematic deviation of the residuals across the observed data range. Unlike for European beech, spatially explicit model for Norway spruce better described HDR variations than its spatially non-explicit counterpart. After DBH, HDOM provided the largest contribution to each model type, followed by DDOM and QMD or CI for both species. The HDR increased with increasing HDOM and CI, but it decreased with increasing DDOM and QMD, suggesting there were significantly large effects of site quality and stand density on HDR. Because of a little difference between the fit statistics and graphical displays of the two model types, spatially non-explicit model is recommended for prediction of HDR for both species as this model does not require spatially explicit CI, which is computationally much more complex than spatially non-explicit competition measures. The proposed HDR models may be applicable to assess stability of trees and stands, and to regulate stand densities.

     

A stochastic model of tree architecture and biomass partitioning: application to Mongolian Scots pines

Background and Aims

Mongolian Scots pine (Pinus sylvestris var. mongolica) is one of the principal species used for windbreak and sand stabilization in arid and semi-arid areas in northern China. A model-assisted analysis of its canopy architectural development and functions is valuable for better understanding its behaviour and roles in fragile ecosystems. However, due to the intrinsic complexity and variability of trees, the parametric identification of such models is currently a major obstacle to their evaluation and their validation with respect to real data. The aim of this paper was to present the mathematical framework of a stochastic functional–structural model (GL2) and its parameterization for Mongolian Scots pines, taking into account inter-plant variability in terms of topological development and biomass partitioning.

Methods

In GL2, plant organogenesis is determined by the realization of random variables representing the behaviour of axillary or apical buds. The associated probabilities are calibrated for Mongolian Scots pines using experimental data including means and variances of the numbers of organs per plant in each order-based class. The functional part of the model relies on the principles of source–sink regulation and is parameterized by direct observations of living trees and the inversion method using measured data for organ mass and dimensions.

Key Results

The final calibration accuracy satisfies both organogenetic and morphogenetic processes. Our hypothesis for the number of organs following a binomial distribution is found to be consistent with the real data. Based on the calibrated parameters, stochastic simulations of the growth of Mongolian Scots pines in plantations are generated by the Monte Carlo method, allowing analysis of the inter-individual variability of the number of organs and biomass partitioning. Three-dimensional (3D) architectures of young Mongolian Scots pines were simulated for 4-, 6- and 8-year-old trees.

Conclusions

This work provides a new method for characterizing tree structures and biomass allocation that can be used to build a 3D virtual Mongolian Scots pine forest. The work paves the way for bridging the gap between a single-plant model and a stand model.     

popRange: a highly flexible spatially and temporally explicit Wright-Fisher simulator

Background

Sequencing and genotyping technology advancements have led to massive, growing repositories of spatially explicit genetic data and increasing quantities of temporal data (i.e., ancient DNA). These data will allow more complex and fine-scale inferences about population history than ever before; however, new methods are needed to test complex hypotheses.

Results

This article presents popRange, a forward genetic simulator, which incorporates large-scale genetic data with stochastic spatially and temporally explicit demographic and selective models. Features such as spatially and temporally variable selection coefficients and demography are incorporated in a highly flexible manner. popRange is implemented as an R package and presented with an example simulation exploring a selected allele’s trajectory in multiple subpopulations.

Conclusions

popRange allows researchers to evaluate and test complex scenarios by simulating large-scale data with complicated demographic and selective features. popRange is available for download at http://cran.r-project.org/web/packages/popRange/index.html.

     

Assessing the effects of architectural variations on light partitioning within virtual wheat–pea mixtures

Background and Aims

Predicting light partitioning in crop mixtures is a critical step in improving the productivity of such complex systems, and light interception has been shown to be closely linked to plant architecture. The aim of the present work was to analyse the relationships between plant architecture and light partitioning within wheat–pea (Triticum aestivum–Pisum sativum) mixtures. An existing model for wheat was utilized and a new model for pea morphogenesis was developed. Both models were then used to assess the effects of architectural variations in light partitioning.

Methods

First, a deterministic model (L-Pea) was developed in order to obtain dynamic reconstructions of pea architecture. The L-Pea model is based on L-systems formalism and consists of modules for ‘vegetative development’ and ‘organ extension’. A tripartite simulator was then built up from pea and wheat models interfaced with a radiative transfer model. Architectural parameters from both plant models, selected on the basis of their contribution to leaf area index (LAI), height and leaf geometry, were then modified in order to generate contrasting architectures of wheat and pea.

Key results

By scaling down the analysis to the organ level, it could be shown that the number of branches/tillers and length of internodes significantly determined the partitioning of light within mixtures. Temporal relationships between light partitioning and the LAI and height of the different species showed that light capture was mainly related to the architectural traits involved in plant LAI during the early stages of development, and in plant height during the onset of interspecific competition.

Conclusions

In silico experiments enabled the study of the intrinsic effects of architectural parameters on the partitioning of light in crop mixtures of wheat and pea. The findings show that plant architecture is an important criterion for the identification/breeding of plant ideotypes, particularly with respect to light partitioning.     

Correcting for bias in distribution modelling for rare species using citizen science data

Aim

To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine‐learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data.

Innovation

Modelling rare species’ distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non‐detections (i.e. class imbalance). Citizen science data can provide a cost‐effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under‐sample the majority class (non‐detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under‐sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor).

Main Conclusions

Spatial under‐sampling increased the accuracy of each model and outperformed the approach typically used to direct under‐sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

     

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Aim

Our aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes.

Location

Arctic shelf area. The Kara Sea.

Methods

Our literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals.

Results

The most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.

Main conclusions

Point process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.     

Pleistocene climatic fluctuations drive isolation and secondary contact in the red diamond rattlesnake (Crotalus ruber) in Baja California

Aim

Many studies have investigated the phylogeographic history of species on the Baja California Peninsula, and they often show one or more genetic breaks that are spatially concordant among many taxa. These phylogeographic breaks are commonly attributed to vicariance as a result of geological or climatic changes, followed by secondary contact when barriers are no longer present. We use restriction‐site associated DNA sequence data and a phylogeographic model selection approach to explicitly test the secondary contact hypothesis in the red diamond rattlesnake, Crotalus ruber.

Location

Baja California and Southern California.

Methods

We used phylogenetic and population clustering approaches to identify population structure. We then used coalescent methods to simultaneously estimate population parameters and test the fit of phylogeographic models to the data. We used ecological niche models to infer suitable habitat for C. ruber at the Last Glacial Maximum (LGM).

Results

Crotalus ruber is composed of distinct northern and southern populations with a boundary near the town of Loreto in Baja California Sur. A model of isolation followed by secondary contact provides the best fit to the data, with both divergence and contact occurring in the Pleistocene. We also identify a genomic signature of northern range expansion in the northern population, consistent with LGM niche models showing that the northern‐most portion of the range of C. ruber was not suitable habitat during the LGM.

Main conclusions

We provide the first explicitly model‐based test of the secondary contact model in Baja California and show that populations of C. ruber were isolated before coming back into contact near Loreto, a region that shows phylogeographic breaks for other taxa. Given the timing of divergence and contact, we suggest that climatic fluctuations have driven the observed phylogeographic structure observed in C. ruber and that they may have driven similar patterns in other taxa.     

Range‐constrained co‐occurrence simulation reveals little niche partitioning among rock‐dwelling Montenegrina land snails (Gastropoda: Clausiliidae)

Aim

Taxon co‐occurrence analysis is commonly used in ecology, but it has not been applied to range‐wide distribution data of partly allopatric taxa because existing methods cannot differentiate between distribution‐related effects and taxon interactions. Our first aim was to develop a taxon co‐occurrence analysis method that is also capable of taking into account the effect of species ranges and can handle faunistic records from museum databases or biodiversity inventories. Our second aim was to test the independence of taxon co‐occurrences of rock‐dwelling gastropods at different taxonomic levels, with a special focus on the Clausiliidae subfamily Alopiinae, and in particular the genus Montenegrina.

Location

Balkan Peninsula in south‐eastern Europe (46N–36N, 13.5E–28E).

Methods

We introduced a taxon‐specific metric that characterizes the occurrence probability at a given location. This probability was calculated as a distance‐weighted mean of the taxon's presence and absence records at all sites. We applied corrections to account for the biases introduced by varying sampling intensity in our dataset. Then we used probabilistic null‐models to simulate taxon distributions under the null hypothesis of no taxon interactions and calculated pairwise and cumulated co‐occurrences. Independence of taxon occurrences was tested by comparing observed co‐occurrences to simulated values.

Results

We observed significantly fewer co‐occurrences among species and intra‐generic lineages of Montenegrina than expected under the assumption of no taxon interaction.

Main conclusions

Fewer than expected co‐occurrences among species and intra‐generic clades indicate that species divergence preceded niche partitioning. This suggests a primary role of non‐adaptive processes in the speciation of rock‐dwelling gastropods. The method can account for the effects of distributional constraints in range‐wide datasets, making it suitable for testing ecological, biogeographical, or evolutionary hypotheses where interactions of partly allopatric taxa are in question.     

Use of predictive habitat modelling to assess the distribution and extent of the current protection of ‘listed’ deep‐sea habitats

Aim

To demonstrate the application of predictive species distribution modelling methods to habitat mapping and assessment of percentage area‐based conservation targets.

Location

The NE Atlantic deep sea (UK and Irish extended continental shelf limits).

Methods

MaxEnt modelling of three listed habitats (Lophelia pertusa (Linnaeus, 1758) reef (LpReef), Pheronema carpenteri (WyvilleThomson, 1869) aggregations (PcAggs) and Syringammina fragilissima (Brady, 1883) aggregations (SfAggs)), with some pre‐selection of variables by generalized additive modelling. Models are validated using repeated 70/30 build/test data splits using AUC and threshold‐dependent assessment methods. Predicted distribution maps are used to assess the adequacy of existing area closures for the protection of listed habitats and to assess percentage representation of each community within existing MPA networks.

Results

Model performances are rated as fair (LpReef), excellent (PcAggs) and good (SfAggs). Current closures are focused on the protection of cold‐water coral reef and incidentally capture some SfAggs suitable environments, but largely fail to protect PcAggs. Considering the wider network of MPAs in the study region, approximately 23% (LpReef), 2% (PcAggs) and 6% (SfAggs) of the area predicted as suitable for each habitat respectively is contained within an MPA.

Main conclusions

To date, decisions on area closures for the protection of ‘listed’ deep‐sea habitats have been based on maps of recorded presence of species that are taken as being indicative of that habitat. Predictive habitat modelling may provide a useful method of better estimating the extent of listed habitats, providing direction for future MPA establishment and a means of assessing MPA network effectiveness against politically set percentage targets. Given the coarse resolution of the model, percentages should be taken as maximal figures, with habitat occurrence likely to be less prevalent in reality.

     

Modelling the Species Distribution of Flat-Headed Cats (Prionailurus planiceps), an Endangered South-East Asian Small Felid

Background

The flat-headed cat (Prionailurus planiceps) is one of the world''s least known, highly threatened felids with a distribution restricted to tropical lowland rainforests in Peninsular Thailand/Malaysia, Borneo and Sumatra. Throughout its geographic range large-scale anthropogenic transformation processes, including the pollution of fresh-water river systems and landscape fragmentation, raise concerns regarding its conservation status. Despite an increasing number of camera-trapping field surveys for carnivores in South-East Asia during the past two decades, few of these studies recorded the flat-headed cat.

Methodology/Principal Findings

In this study, we designed a predictive species distribution model using the Maximum Entropy (MaxEnt) algorithm to reassess the potential current distribution and conservation status of the flat-headed cat. Eighty-eight independent species occurrence records were gathered from field surveys, literature records, and museum collections. These current and historical records were analysed in relation to bioclimatic variables (WorldClim), altitude (SRTM) and minimum distance to larger water resources (Digital Chart of the World). Distance to water was identified as the key predictor for the occurrence of flat-headed cats (>50% explanation). In addition, we used different land cover maps (GLC2000, GlobCover and SarVision LLC for Borneo), information on protected areas and regional human population density data to extract suitable habitats from the potential distribution predicted by the MaxEnt model. Between 54% and 68% of suitable habitat has already been converted to unsuitable land cover types (e.g. croplands, plantations), and only between 10% and 20% of suitable land cover is categorised as fully protected according to the IUCN criteria. The remaining habitats are highly fragmented and only a few larger forest patches remain.

Conclusion/Significance

Based on our findings, we recommend that future conservation efforts for the flat-headed cat should focus on the identified remaining key localities and be implemented through a continuous dialogue between local stakeholders, conservationists and scientists to ensure its long-term survival. The flat-headed cat can serve as a flagship species for the protection of several other endangered species associated with the threatened tropical lowland forests and surface fresh-water sources in this region.