Species-habitat Relationships Research Articles

Spatial distribution of sandeel (Hyperoplus lanceolatus) and implications for monitoring marine protected sites

Increased human demand on the marine environment and associated biodiversity threatens sustainable delivery of ecosystem goods and services, particularly for shallow shelf-sea habitats. As a result, more attention is being paid to quantifying the geographical range and distribution of seabed habitats and keystone species vulnerable to human pressures. In this study, we develop a workflow based on unsupervised K-Means classification units and Generalized Linear Models built from multi-frequency backscatter analyses (95, 300 kHz), bathymetry and bathymetry derivatives (slope) to predict different levels of sandeel densities in Hempton's Turbot Bank Special Area of Conservation (SAC). For Hyperoplus lanceolatus densities, the performance of single frequency verses multi-frequency models is compared. Relatively high agreement between K-Means clustering outputs (from 95 kHz and multi-frequency models) and ground-truthed sandeel densities is noted. Moreover, Root Mean Squared Error (RMSE) values in this instance demonstrate that single-frequency models are favoured over the multi-frequency model in terms of predictive ability. This is mostly linked to the species strong affinity for sedimentary environments whose variability is better captured by the lower frequency system. Generally, these results provide important information about species-habitat relationships and pinpoint bedform features where sandeels are likely to be found and whose variability is potentially linked to the bathymetry domain. The workflow developed in this study also provides a proof of concept to support the design of a robust species-specific monitoring plan in marine protected areas. Most importantly, we highlight how decisions made during sampling, data handling, analysis could impact the final outputs and interpretation of Species Distribution Models and benthic habitat mapping.

Biodiversity on the line: life cycle impact assessment of power lines on birds and mammals in Norway

The global shift towards renewable energy plays an important role in fighting climate change. To facilitate the global growth of renewable energy production, the expansion of the electric grid becomes inevitable. Yet further construction of power lines poses a risk to biodiversity. Power lines traverse natural habitats and can lead to habitat conversion, fragmentation, and loss. Moreover, due to collisions and electrocutions, power lines kill hundreds of millions of birds each year. These impacts, however, have so far not been incorporated into decision-making. Life cycle assessment (LCA) is a widely used framework to compare environmental impacts and support decision-makers in planning and promoting sustainable strategies. We adapted existing life cycle impact assessment (LCIA) models to quantify the three main impacts of power lines on biodiversity: collision, electrocution, and habitat conversion and fragmentation. Our models incorporated species-area and species-habitat relationships to assess the effects of power lines on the diversity of birds and non-flying mammals in Norway, as the country is currently committed to reducing its emissions by expanding its renewable energy capacity. Overall, habitat conversion and fragmentation had the highest impact across the three impact categories, particularly affecting mammal richness. Furthermore, distribution lines often affected species richness more than transmission lines. The effect of the three impact pathways varied among different species groups, highlighting the vulnerability of certain species to habitat change, collision, and electrocution. Integrating LCIA models that quantify the impacts of power lines on biodiversity into LCA can support decision-makers with tools to promote the development of the electric grid without overlooking its effect on species richness. In addition, our models set the stage for a comprehensive assessment of the effects of electricity generation and transmission on biodiversity.

Using Bayesian multispecies models to evaluate fish and invertebrate detection probability and distribution in the hypersaline Bahia Grande tidal basin

AbstractObjectiveIn 2000, the Laguna Atascosa National Wildlife Refuge acquired the Bahia Grande (Texas) management unit, a space that had lain barren and arid for 70 years. A large cooperative partnership launched a restoration project to replenish the basin and recover its original tidal hydrology. In 2005, the construction of a pilot channel successfully restored water throughout the basin, and plans to eventually widen the channel were developed. Our study aims to evaluate an estuarine habitat restoration by assessing ecological drivers and the impacts on species diversity.MethodsWe evaluated species richness, detection/occupancy rates, and species–habitat relationships, and we estimated the sampling effort required to achieve a given level of relative precision if relative abundance was used instead of occupancy to inform future sampling. Sampling gear included bag seines for juvenile life stages and gill nets for capturing subadult and adult life stages. For analysis, we used a Bayesian negative binomial linear mixed‐effects model to evaluate richness–habitat relationships and a hierarchical Bayesian multispecies model to evaluate individual species–habitat relationships, and we calculated the total number of fish captured and relative standard error by gear and sample year to produce a precise estimate of relative abundance.ResultOverall, 29 species were caught between 2018 and 2021. Salinity emerged as a clear driver in the Bahia Grande, as both species richness and individual‐level responses were negatively associated with high salinity values. We found that catch estimated as relative abundance had much variability, as is typical of most survey programs assuming constant detectability, and the number of net sets or seine hauls required to achieve a given level of relative precision varied considerably depending on the species, season, year, and gear type. The most collected species were found in the upper extremes of their salinity tolerances—potentially a unique adaptation to this hypersaline system.ConclusionBaseline data suggest that for the channel widening to be successful, there must be a noticeable increase in suitable habitat characteristics throughout the basin.

Differentially biased sampling strategies reveal the non-stationarity of species distribution models for Indian small felids

Species Distribution Models (SDMs) have been used extensively to understand species-habitat relationships and design conservation strategies. The ability to train these models using a wide variety of datasets and modelling algorithms has led to their wide applicability across systems. However, the ease of modelling also leads to their use as off-the-shelf models without a detailed investigation of the data and their suitable end-use application. The effect of various modelling parameters on inferences has been explored, however, their interaction with training data type is limited. We used country-wide data for four sympatric Indian small cat species to understand the sensitivity of SDMs to data types, sampling extents and their interaction. Our results reveal the non-stationarity of models with varying modelling parameters. The extent of the training dataset had major implications on the inferences and interacted strongly with the type of dataset used. The divergent distribution of the target species revealed that the effect of sampling extent was more pronounced for species that have limited distribution within the predictive extent. Lastly, our results highlight the significance of sampled environmental space in explaining the non-stationarity of the model outputs.

Habitat use of anadromous and amphidromous sturgeons in North America: a systematic review

Sturgeons are among the most endangered fishes in the world. Identifying habitat use characteristics to inform restoration projects is crucial for recovery. However, small sample sizes, inadequate replication of studies, and limited spatial extents complicate our ability to effectively apply the findings of single studies to endangered species conservation across the larger riverscape. We synthesized information from amphidromous and anadromous sturgeons in North America to identify species-specific knowledge gaps and conduct a quantitative comparison of species–habitat relationships. We provided a qualitative summary of substrate use and synthesized estimates of depth and velocity during spawning and non-spawning activity. Generalized patterns among species were identified, such as spawning in fast water on hard substrate and then using slow water with soft substrate areas when not spawning. We noted species-specific variability during spawning that may be attributed to historical maximum length, egg characteristics, and watershed features. This study provides some of the first estimates of habitat use that can be adapted for many populations. Results can contribute to empirically grounded decision-support tools used to prioritize information needs for recovery.

Variable importance and scale of influence across individual scottish wildcat hybrid habitat models

Understanding the scale dependence of species-habitat relationships is an important area of research in species distribution modeling. There has been little research focused on how habitat selection may depend on individual variation among organisms, geographical location and ecological context of that location. Furthermore, little is known about the extent and drivers of heterogeneity of scale dependence among individuals of a species inhabiting different ecological contexts, and few studies have compared scale dependence and variable importance in a spatially replicated framework. Two of the most important factors for interpreting habitat relationships models include: (1) the relative importance of variables in the model and (2) the spatial scale at which each variable has the largest influence. Based on the existing evidence we hypothesize a priori that landcover variables will generally be the most important predictors, followed by topography, then soil type (which influence both vegetation and prey), Normalized Difference Vegetation Index (NDVI) as an indicator of total vegetation density and perhaps a proxy for prey density, vegetation cover and rabbit abundance. We also expected a priori that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. We expected topographical features to be selected at broad scales, as they influence broad-scale climatic and ecological conditions. We also expected that land cover classes and vegetation cover density to be selected at relatively broad scales given past research showing land cover generally influences habitat selection at relatively broad scales. We expected NDVI and soil type to be selected at finer scales, as their variation influences the distribution of resources and limiting conditions within landscapes. Finally, we expected that rabbit abundance and linear features would affect wildcat hybrid occurrence at the finest scales, given these are resources and conditions that vary over short distances and strongly influence wildcat and wildcat hybrid behavior and habitat use. Our results were consistent with the hypothesis that there may be consistency regarding which variables or variable groups are most important as predictors of wildcat hybrid occurrence in Scotland. Based on previous research we expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. Finally, our results identify a clear and consistent trend of increasing frequency of inclusion of variables at increasingly broad scales. This is a linear trend in frequency of variables retained increasing as the scale increased. This suggests a consistent and monotonic pattern of more frequent retention of variables at increasingly broad scales.

Untangling Coastal Diversity: How Habitat Complexity Shapes Demersal and Benthopelagic Assemblages in NW Iberia

Understanding species–habitat relationships is essential for ecosystem-based conservation. This study explores the significance of habitat characteristics and complexity for demersal and benthopelagic communities within a patchwork of coastal habitats, including rocky seabed, macroalgae formations, sandy bottoms, and a combination of rock and sand areas. Species and habitats were surveyed along the north-west (NW) Iberian continental shelf area of Viana do Castelo using baited remote underwater video stations (BRUVS). We found significant differences (p < 0.05) in species assemblages across habitats, with rocky substrates showing the highest diversity and abundance. Sand habitats showed the lowest species richness and abundance, underscoring the importance of habitat complexity to support marine life. Our study also emphasises the role of specific species in shaping the communities, identifying key species such as Trisopterus luscus, Diplodus vulgaris, and Ctenolabrus rupestris as the three most abundant in the region and significant contributors to the observed dissimilarities between habitats. By elucidating the impact of habitat complexity on marine life, our results offer essential baseline data, which serve as a kick-start point to inform sustainable management and conservation strategies for the long-term health and productivity of these vital ecological systems in the North-East Atlantic.

Temporal variables improve a spatiotemporal species distribution model for the non-native freshwater fish Candidia temminckii

Ecosystem conservation requires a deeper understanding of species-habitat relationships and population dynamics at a fine spatiotemporal resolution. We propose a new distribution modeling method based on a 5-year monthly survey that considers the temporal continuity of species distributions and physical habitat datasets by inputting continuous time-related variables. We employed random forests to relate the presence/absence of the non-native freshwater fish Candidia temminckii to physical habitat data at 15 sampling sites along a 1.4km spring-fed river in Japan. The proposed method outperforms all conventional methods using datasets split into a specific time period to incorporate temporality into the model. The order of variable importance and shape of the partial dependence plots of the proposed method reflect species ecology and show a gradual shift over time compared to the conventional methods. These results demonstrate the applicability of the proposed method to species distribution modeling using fine-scale spatiotemporal data.

An easily implemented single-visit survey method for intermittently available and imperfectly detectable wildlife applied to the Florida east coast diamondback terrapin (Malaclemys terrapin tequesta).

Single-visit surveys of plots are often used for estimating the abundance of species of conservation concern. Less-than-perfect availability and detection of individuals can bias estimates if not properly accounted for. We developed field methods and a Bayesian model that accounts for availability and detection bias during single-visit visual plot surveys. We used simulated data to test the accuracy of the method under a realistic range of generating parameters and applied the method to Florida's east coast diamondback terrapin in the Indian River Lagoon system, where they were formerly common but have declined in recent decades. Simulations demonstrated that the method produces unbiased abundance estimates under a wide range of conditions that can be expected to occur in such surveys. Using terrapins as an example we show how to include covariates and random effects to improve estimates and learn about species-habitat relationships. Our method requires only counting individuals during short replicate surveys rather than keeping track of individual identity and is simple to implement in a variety of point count settings when individuals may be temporarily unavailable for observation. We provide examples in R and JAGS for implementing the model and to simulate and evaluate data to validate the application of the method under other study conditions.

Guidelines for the use of spatially varying coefficients in species distribution models

AbstractAimSpecies distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks.InnovationHere we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (Ammodramus savannarum) occurrence across the continental United States.Main conclusionsWe found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation.

Interactions among rainfall, fire, forbs and non-native grasses predict occupancy dynamics for the endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a Mediterranean-type ecosystem

It is important to understand species-habitat relationships to implement effective adaptive management for rare species. However, it can be challenging to assess habitat associations and their relationships to abiotic stressors in dynamic habitats without the insights that can be gained from long-term monitoring. We report results from the first six years of extensive track tube monitoring of the largest two of three remaining extant populations of federally endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a coastal Mediterranean-type ecosystem on Marine Corps Base, Camp Pendleton in southern California, USA. We used dynamic occupancy and structural equation modeling to assess potential drivers of population trends that included habitat, fire history, rainfall, disturbance, and the presence of other small mammals. We found that the variables that best predicted mouse occupancy were moderate to high forb and perennial herb cover (40–80%), and moderate to high open ground (20–70%) and low non-native grass cover (<20%), Non-native grass cover (>20%) was also a strong predictor of lower PPM colonization and increased extinction probabilities, with the extent of non-native grass cover being strongly influenced by annual rainfall and recency of fire. Our study adds to the growing literature on effects of invasive annual grasses on native species in Mediterranean-type ecosystems. We suggest that habitat management could be based upon promotion of open forb and perennial herb dominated habitats with reduction of non-native grasses by prescribed fire and other methods. These types of spatial and temporal monitoring programs can support land managers by creating a monitoring and management feedback loop. They can reveal landscape and environmental variables associated with species persistence, inform habitat management goals, and help managers to assess the success of management actions on populations of conservation concern.

Using species distribution modeling to generate relative abundance information in socio-politically unstable territories: Conservation of Felidae in the central-western region of Mexico.

The distribution range and population abundance of species provide fundamental information on the species-habitat relationship required for management and conservation. Abundance inherently provides more information about the ecology of species than do occurrence data. However, information on abundance is scarce for most species, mainly at large spatial scales. The objective of this work was, therefore, to provide information regarding the population status of six wild felids inhabiting territories in Mexico that are inaccessible or politically unstable. This was done using species distribution models derived from occurrence data. We used distribution data at a continental scale for the wild felids inhabiting Mexico: jaguar (Panthera onca), bobcat (Lynx rufus), ocelot (Leopardus pardalis), cougar (Puma concolor), margay (Leopardus wiedii), and jaguarundi (Herpailurus yagouaroundi) to predict environmental suitability (estimated by both Maxent and the distance to niche centroid, DNC). Suitability was then examined by relating to a capture rate-based index, in a well-monitored area in central western Mexico in order to assess their performance as proxies of relative abundance. Our results indicate that the environmental suitability patterns predicted by both algorithms were comparable. However, the strength of the relationship between the suitability and relative abundance of local populations differed across species and between algorithms, with the bobcat and DNC, respectively, having the best fit, although the relationship was not consistent in all the models. This paper presents the potential of implementing species distribution models in order to predict the relative abundance of wild felids in Mexico and offers guidance for the proper interpretation of the relationship between suitability and population abundance. The results obtained provide a robust information base on which to outline specific conservation actions and on which to examine the potential status of endangered species inhabiting remote or politically unstable territories in which on-field monitoring programs are not feasible.

A case study on joint species distribution modelling with bird atlas data: Revealing limits to species' niches

Growing interest in biodiversity mapping has spurred the development of species distribution atlases, often mainly based on citizen-science projects. Atlas data have been frequently exploited to model species' ecological niches and distributions on a species-by-species basis. However, spatial autocorrelation and phylogenetic relatedness among species complicate the statistical description of species' niches. Also, the effects of species' traits and co-occurrences on species-habitat relationship are commonly disregarded. In this work, we build a hierarchical multi-species model based on a major citizen-science project (the third Spanish breeding bird atlas) that simultaneously accounts for spatial, phylogenetic and trait-based dependencies. We predict the distributions of species niches, species richness and community traits along regional ecological gradients. Climate, habitat associations and species' traits all contribute (in this order) to structuring species' distributions. Species richness increases towards intermediate climatic conditions and with aquatic habitat cover and decreases with increasing forest and woody agricultural land cover. Species were distributed along regional climate gradients in accordance with their global thermal niches. Forest habitats favoured assemblages dominated by generalist, small-sized and cold-dwelling species with limited migratory behaviour. Increasing sampling effort augmented the model performance. Model performance was weaker for rare species and those with decreasing population sizes, likely due to their low niche saturation. Overall, we show that ecological relationships generalize from local to large scales and may be eludicated from atlases based on citizen-science mapping efforts.

Data fusion of distance sampling and capture-recapture data

Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species–habitat relationships and abundance across space and time. Distance sampling (DS) and capture-recapture (CR) are two widely collected data types to learn about species–habitat relationships and abundance; still, they are seldomly used in SDMs due to the lack of spatial coverage. However, data fusion of the two data sources can increase spatial coverage, which can reduce parameter uncertainty and make predictions more accurate, and therefore, can be used with SDM. We developed a model-based approach for data fusion of DS and CR data. Our modeling approach accounts for two common missing data issues: 1) individuals that are missing not at random (MNAR) and 2) partially missing location information. Using a simulation experiment, we evaluated the performance of our modeling approach and compared it to existing approaches that use ad-hoc methods to account for missing data issues. Our results show that our approach provides unbiased parameter estimates with increased efficiency compared to the existing approaches. Finally, we demonstrated our approach using data collected for Grasshopper Sparrows (Ammodramus savannarum) in north-eastern Kansas, USA.

Using aerial LiDAR to assess regional availability of potential habitat for a conservation dependent forest bird

Remotely-sensed data can inform conservation efforts that target forest wildlife, however, few spatial data products are able to quantify fine-scale aspects of structural variation within forests. Increased availability of Light Detection and Ranging (LiDAR) datasets that cover broad spatial extents and ownership types (e.g., entire states) provide useful information regarding canopy and understory structure within forested landscapes. The fusion of LiDAR data with field-based species surveys can advance our understanding of species-habitat relationships and improve the effectiveness of conservation programs to recover habitat-limited species. The Golden-winged Warbler (Vermivora chrysoptera) is a forest-dependent songbird that nests in structurally-complex young forest across eastern North America. As with many early-successional obligates, this species has been declining for decades due, in part, to the steady loss of young forest/shrubland nesting habitat. Although conservation programs have begun restoring Golden-winged Warbler habitat, these efforts are currently limited by the inability to identify existing habitat across large spatial extents and diverse ownership patterns. Recent availability of state-wide LiDAR data for Pennsylvania provides an opportunity to overcome this limitation. From 2019 to 20, we surveyed for Golden-winged Warblers and structural vegetation at 837 sites across six forest blocks in eastern Pennsylvania. We combined these data with LiDAR derived forest structural metrics to develop statistical models to predict patterns of occupancy. Golden-winged Warbler occupancy probability was explained by models containing several LiDAR-derived structural metrics (e.g., percentage of first returns between 1 and 5 m in height, structural complexity, etc.). Moreover, models fit with LiDAR-derived covariates predicted occupancy much better than those using only field-measured vegetation covariates (ΔAICc = 53.27). Mapped predictions of Golden-winged Warbler occupancy revealed potential habitat (especially regenerating timber harvests) on both private and public lands. These results demonstrate the efficacy of LiDAR for modeling forest bird habitat associations, and how such data sources can provide a valuable tool for conservation planning.

Combining acoustic tracking and LiDAR to study bat flight behaviour in three-dimensional space

BackgroundHabitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ.MethodsHere, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats’ positions and vegetation structures.ResultsOur results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light.ConclusionBy combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction.

Modelling fish co-occurrence patterns in a small spring-fed river using a machine learning approach

Stopping biodiversity loss due to human activities is of great concern for ensuring ecosystem services for sustainable development. While species-habitat relationships are an important aspect of species conservation, considering species interactions remains challenging in habitat suitability modelling due to the complex nature of the species’ ecology and uncertainties present in ecological data. To cope with this challenge, a Random Forests (RF) multi-class classifier is built using a data set comprised of monthly fish habitat surveys conducted over a period of two years for analyzing the 16 co-occurrence patterns among four fish species, including endangered species and translocated domestic alien species, in a small spring-fed river in Japan. This multi-class habitat suitability model allows for assessing the instream habitat conditions under which multiple species co-occur. Variable importance and partial dependence plots are generated to extract ecological information for a deeper understanding of the species co-occurrence patterns. Results show that the model performance is greatly influenced by data prevalence as reported for single-species habitat suitability models. Ecological information extracted from the RF model illustrates instream habitat conditions that are important for each of the co-occurrence patterns. Such information can be used for prioritizing conservation sites and better designing habitat conditions in order for multiple native species to co-occur or for preventing non-native species to expand their distribution ranges.

Conservation potential of non-protected area for sympatric carnivores in Bhutan

Carnivore conservation in Bhutan is mainly focused on charismatic species such as the tiger and the snow leopard inside protected areas. Little is known about the conservation status of small and medium-sized sympatric carnivores outside the protected areas. We aim to investigate the effects of environmental and anthropogenic variables on the occupancy of three sympatric carnivores: dhole (Cuon alpinus), Asiatic golden cat (Catopuma temminckii), and leopard cat (Prionailurus bengalensis) and predict their distribution in a key non-protected area of Bhutan. We used detection/non-detection data from a camera trap survey conducted in 2019–2020 in Sarpang, Bhutan. We employed detection-corrected occupancy models to assess the species-habitat relationship and predict their spatial distribution. The responses were nuanced among individuals and at different spatial scales. Elevation was associated with the decline in habitat use probability of golden cats and leopard cats. Forest cover positively influenced golden cat and leopard cat habitat use but negatively for dholes. Dholes were less likely to use habitats close to human settlement. Our results indicate that forests in non-protected areas could offer a high potential for carnivore conservation. Our findings suggest that the patterns of sympatry among carnivores are also mediated by prey abundance and anthropogenic variables. Taken together, we show that carnivores are negatively affected by human settlement density at a relatively fine-scale (1 km) and particularly dholes were likely to avoid habitat interspersed with human settlement. Multi-carnivore conservation in non-protected areas can be achieved by minimizing land use change, limiting forest conversion and protecting prey species. A holistic approach to management that balances conservation and human development may be needed for the conservation of sympatric carnivores in an increasingly human-modified landscape.

Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF.

Thresholds in the Species-Area-Habitat Model: Evidence from the Bryophytes on Continental Islands.

To clarify whether (1) there are thresholds in the species-area-habitat relationship for bryophytes and potential mechanisms, (2) such thresholds vary among different bryophyte groups, and (3) choros is better than area or habitat alone in the prediction of SR. Islands in central and southern Zhejiang, China. We investigated the species richness (SR) of five bryophyte groups (total bryophytes, total mosses, liverworts, acrocarpous mosses, and pleurocarpous mosses) and habitat types on 66 islands. By using four threshold models, the logarithmic and the power models, we quantified their SR-choros relationships (SKRs), species-area relationships (SARs), and species-habitat relationships (SHRs). We also conducted path analyses to detect the direct effects of area per se and habitat per se on the SR. The AICc values of the SKR models were overall smaller than those of the respective SAR and SHR models. The left-horizontal two-threshold model was best for the SKRs. A phenomenon (the small-choros effect, SCE) in which SR independently varied choros below a given threshold was detected. The SCE thresholds were smaller in mosses than in liverworts and in acrocarpous mosses than in pleurocarpous mosses. No direct and positive effects of habitat per se on the SR were detected below choros thresholds for all five groups. There were two thresholds and SCEs in the SKRs of all five bryophyte groups. The SCEs likely resulted from the elimination of the direct and positive effects of habitat diversity on the SR of the bryophytes on small choros islands. The SCE thresholds were high for species groups sensitive to environments. Choros was better than area or habitat alone in determining the SR of the bryophytes on continental islands.