Probability Of Species Occurrence Research Articles

Modeling the species occurrence probability and response of climate change on Himalayan Somalata plant under different Shared Socioeconomic Pathways.

In this study, the current distribution probability of Ephedra gerardiana (Somalata), a medicinally potent species of the Himalayas, was assessed, and its spatial distribution change was forecasted until the year 2100 under three Shared Socioeconomic Pathways. Here, we used the maximum entropy model (MaxEnt) on 274 spatially filtered occurrence data points accessed from GBIF and other publications, and 19 bioclimatic variables were used as predictors against the probability assessment. The area under the curve, Continuous Boyce Index, True Skill Statistics, and kappa values were used to evaluate and validate the model. It was observed that the SSP5-8.5, a fossil fuel-fed scenario, saw a maximum habitat decline for E. gerardiana driving its niche towards higher altitudes. Nepal Himalayas witnessed a maximum decline in suitable habitat for the species, whereas it gained area in Bhutan. In India, regions of Himachal Pradesh, Uttarakhand, Jammu and Kashmir, and Sikkim saw a maximum negative response to climate change by the year 2100. Mean annual temperature, isothermality, diurnal temperature range, and precipitation seasonality are the most influential variables isolated by the model that contribute in defining the species' habitat. The results provide evidence of the effects of climate change on the distribution of endemic species in the study area under different scenarios of emissions and anthropogenic coupling. Certainly, the area of consideration encompasses several protected areas, which will become more vulnerable to increased variability of climate, and regulating their boundaries might become a necessary step to conserve the regions' biodiversity in the future.

Estimating risk to prevent damage: predicting and preventing coypu (Myocastor coypus) damage to transport infrastructure.

A major impact of invasive Myocastor coypus in their introduction range is the collapse of riverbanks and nearby infrastructure, such as railway lines, due to the species' burrowing activities. Because widespread implementation of preventive measures along watercourses is unfeasible, identifying susceptible areas is key to guide targeted management actions. This study used species-habitat models to: (i) identify local environmental features of the railway line/watercourse intersections (RLWIs) that make them particularly susceptible to coypu damage, and (ii) predict species occurrence probability over a wide lowland-hilly area of northern Italy (Lombardy) to identify priority areas for monitoring. Local-scale models identified that the RLWIs most susceptible to burrowing were those surrounded by arable land with interspersed hedgerows locally characterized by high herbaceous vegetation and clay soil. In urbanized areas and areas of intensive agriculture, coypu dens were generally located significantly closer to the railway, increasing the risk of collapse. A landscape-scale species distribution model showed that lowland areas along major rivers and lake shores, and also agricultural areas with a dense minor hydrographic network, particularly in the southeast of the study area, are more likely to be occupied by coypu. Local-scale models showed that specific environmental characteristics increase the risk of burrowing near RLWIs. The landscape-scale model allowed us to predict which areas require thorough monitoring of RLWIs to search for such local characteristics to implement preventive management measures. The proposed model-based framework can be applied to any geographical context to predict and prevent coypu damage. © 2024 Society of Chemical Industry.

Climatic niche modelling and genetic analyses highlight conservation priorities for the Spotted Softshell Turtle (Pelodiscus variegatus)

The Spotted Softshell Turtle (Pelodiscus variegatus) has been recognised since 2019 from Vietnam and Hainan Island, China, but little information about its population status and distribution range is currently available. The species has been provisionally listed as Critically Endangered by the Turtle and Tortoise Working Group, although the status has not been officially accepted by the IUCN, due to the threats the species is facing, including habitat loss and degradation, overexploitation for food, competition with other non-native softshell turtles and pollution. To identify conservation priority sites for P. variegatus in mainland Indochina, this study combines molecular analyses and species distribution modelling. Our results show that, in Vietnam, Phong Nha-Ke Bang National Park has the largest suitable area and high probability of species occurrence, followed by Vu Quang National Park and Song Thanh and Ke Go Nature Reserves. In addition, the central provinces, from Thanh Hoa to Thua Thien Hue in Vietnam, constitute a key part of the species distribution and should be prioritised for conservation actions. According to the study’s findings, although P. variegatus is possibly found in Laos, the probability decreases sharply at the border between both countries and there is also a gap in the occurrence of wetlands, arguing for strong natural barriers. Unfortunately, to date, only part of the species potential distribution is protected, while no records are known from protected areas, highlighting the need for extended or even new reserves. To recover natural populations of the species and following the IUCN’s One Plan Approach to Conservation, breeding programmes have been established in Vietnam with a potential to expand to other facilities in the country and abroad. Once suitable sites are identified, offspring can be released into the protected areas to improve the current conservation status of this highly-threatened softshell turtle.

Distribution modelling of the Caucasian endemic Fritillaria latifolia against the background of climate change

Current climate change, habitat degradation, pastoralism, shoot and bulb harvesting pose serious threats to the rare Caucasian endemic Fritillaria latifolia throughout its range. Knowledge of the limiting factors, species range dynamics in relation to climate change and the role of Protected Areas in species distribution are necessary to develop an effective conservation system at present and in the future. This was aimed (1) to determine the most suitable set of abiotic predictors for modelling Fritillaria latifolia localisation, (2) to formalise environmental and anthropogenic factors in species distribution models, (3) to predict the possible changes in the species range in relation to climatic changes, (4) to identify refugia with a consistently high probability of the species occurrence despite climatic changes. We applied Maxent software for species habitat modelling to build current and climatic models of the Fritillaria latifolia distribution, considering the abiotic variables and anthropogenic predictors such as the distance to Protected Areas and grasslands. Distances to anthropogenic infrastructure were calculated with the Path Distance measure considering the horizontal straight-line distance, surface distance and vertical factor. We also formalised the area accessibility (movement factor) through the distance to optimal sites (plots with 0.8 threshold of habitat suitability), where the probability of species occurrence was higher than 0.5. The most important abiotic variables in the species distribution were the Emberger's pluviothermic quotient, with optimal values corresponding to humid and perhumid climates, and the terrain roughness index, with optimal values ranging from nearly level (81–116) to intermediately rugged (162–239) slopes. Distance to Protected Areas (0–1 km) was the third important predictor of the Fritillaria latifolia current distribution, while the distance to grasslands contributed less to the model. The distance of suitable areas from optimal habitats (area accessibility) was 15 km. The species current core ranges are localised in the Western and Central Caucasus, Western and Central Transcaucasia, and the northwestern ridges of the Lesser Caucasus within a network of Protected Areas covering most of the highlands. The optimistic socio-economic pathway SSP1-2.6 predicted a 1.6-fold decrease in the area of species optimal habitats from 2021 to 2100. The pessimistic SSP5-8.5 scenario predicted 122-fold habitat area reduction. According to SSP1-2.6 climatic models, by 2100 the refugia area would be 172.4 km2 in the highlands of the western and central parts of the Greater Caucasus, including the Caucasus State Nature Reserve and Teberda National Park. These areas should be prioritised for the conservation of Fritillaria latifolia populations.

A novel approach for improving the spatiotemporal distribution modeling of marine benthic species by coupling a new GIS procedure with machine learning

In this study we developed and validated a new integrative method for constructing and assessing Species Distribution Models (SDMs) for marine benthic species across spatial and temporal scales. This methodology synthesizes four dimensions-latitude, longitude, depth and time-into a unified predictive output, achieved by integrating a novel GIS tool, Bathymetric Projection (BP) with machine learning algorithms. BP can extract multi-dimensional data from remote sensing and satellite-derived datasets across the oceanic water column, including latitude, longitude, depth, and time. This data is then mapped onto the ocean floor using a reference bathymetric layer. The resulting seafloor layer is a predictive variable within the SDMs for benthic species. The model output quantifies the probability of species occurrence, or habitat suitability, as determined by environmental conditions specific to the ocean floor across the four dimensions. We validated this approach using two benthic species from the Colombian Pacific Ocean: the White shrimp (Litopenaeus occidentalis) and the Coliflor shrimp (Solenocera agassizii). The model's performance ranged from 87% to 98%, as indicated by 30-fold cross-validation on test datasets. Depth ranges and monthly seasonal patterns were accurately predicted when compared to independent literature data, confirming the model's capability to generate precise spatiotemporal distributions for benthic species. This research underscores the necessity of incorporating depth and time into SDMs, given their critical interactions with latitude and longitude in determining species distribution. The methodology offers a valuable tool for informing decision-making processes in fisheries and environmental management by enhancing the predictive accuracy of spatiotemporal distributions for benthic species, thereby facilitating more holistic interpretations of habitat suitability.

Wood decomposition is increased by insect diversity, selection effects, and interactions between insects and microbes.

Biodiversity drives ecosystem processes, but its influence on deadwood decomposition is poorly understood. To test the effects of insect diversity on wood decomposition, we conducted a mesocosm experiment manipulating the species richness and functional diversity of beetles. We applied a novel approach using computed tomography scanning to quantify decomposition by insects and recorded fungal and bacterial communities. Decomposition rates increased with both species richness and functional diversity of beetles, but the effects of functional diversity were linked to beetle biomass, and to the presence of one large-bodied species in particular. This suggests that mechanisms behind observed biodiversity effects are the selection effect, which is linked to the occurrence probability of large species, and the complementarity effect, which is driven by functional differentiation among species. Additionally, beetles had significant indirect effects on wood decomposition via bacterial diversity, fungal community composition, and fungal biomass. Our experiment shows that wood decomposition is driven by beetle diversity and its interactions with bacteria and fungi. This highlights that both insect and microbial biodiversity are critical to maintaining ecosystem functioning.

Rooting depth and xylem vulnerability are independent woody plant traits jointly selected by aridity, seasonality, and water table depth.

Evolutionary radiations of woody taxa within arid environments were made possible by multiple trait innovations including deep roots and embolism-resistant xylem, but little is known about how these traits have coevolved across the phylogeny of woody plants or how they jointly influence the distribution of species. We synthesized global trait and vegetation plot datasets to examine how rooting depth and xylem vulnerability across 188 woody plant species interact with aridity, precipitation seasonality, and water table depth to influence species occurrence probabilities across all biomes. Xylem resistance to embolism and rooting depth are independent woody plant traits that do not exhibit an interspecific trade-off. Resistant xylem and deep roots increase occurrence probabilities in arid, seasonal climates over deep water tables. Resistant xylem and shallow roots increase occurrence probabilities in arid, nonseasonal climates over deep water tables. Vulnerable xylem and deep roots increase occurrence probabilities in arid, nonseasonal climates over shallow water tables. Lastly, vulnerable xylem and shallow roots increase occurrence probabilities in humid climates. Each combination of trait values optimizes occurrence probabilities in unique environmental conditions. Responses of deeply rooted vegetation may be buffered if evaporative demand changes faster than water table depth under climate change.

Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs

Predicting the occurrence probability of species is intrinsically dependent on the quality of the training dataset and, in particular, on the sample prevalence (i.e., the ratio between presences and absences). Whenever the number of presences and absences is not equal within the training dataset, the predictions deviate towards higher values as the sample prevalence increases and vice versa. As a result, probability models of species occurrence with different sample prevalence cannot be directly compared. The favourability concept was introduced to amend this limitation. Indeed, the favourability – i.e., the variation in the probability of occurrence regardless the sample prevalence – could reduce the degree of uncertainty when comparing species distributions despite different sample prevalences. To test this hypothesis, we simulated 50 virtual species and compared the predictive performance of four probability-based and favourability-based Species Distribution Models (GLM, GAM, RF, BRT) under a set of different prevalence values and sampling strategies (i.e, random and stratified sampling). Favourability-based models performed slightly better than probability-based models in predicting the species distribution over geographic space, confirming also their capability to reduce the variability of the predictions across different degrees of sample prevalence.

Assessment of soil compaction and rutting in managed forests through an airborne LiDAR technique

AbstractTo ensure sustainable forest management, the assessment and monitoring of soil compaction and rutting are essential. Here, we used airborne light detection and ranging‐derived digital terrain model (LiDAR‐derived DTM), available for the forest of Compiègne in northern France, to compute a spatial index of soil rutting. Following an environmental systematic sampling design, we selected 45 plots representative of the forest stand conditions where we subsequently extracted information from the DTM to compute the cumulative length of ruts (CLR). To assess the quality of this LiDAR‐derived index, we related the CLR index to in‐situ soil and vegetation parameters such as soil texture, soil pH, and understory plant species composition. Floristic surveys were carried out across all 45 plots to generate plant species response curves along the CLR gradient. We found soil texture, soil type, and soil pH to be important determinants of CLR. For instance, CLR was the highest in soils with the highest clay content. A total of 22 out of the 94 understory plant species we analyzed showed a significant response curve along the CLR gradient. Most important, the occurrence probability of species associated with wet soils and stagnant waters (e.g., Juncus effusus), like those found in ruts, increased with CLR. Other species associated with dry soils (e.g., Hedera helix) showed a negative response curve along the CLR gradient. In conclusion, the proposed index (CLR) has proven useful to remotely assess soil compaction and rutting after logging operations.

Modeled Vegetation Community Trajectories: Effects from Climate Change, Atmospheric Nitrogen Deposition, and Soil Acidification Recovery.

Forest understory plant communities in the United States harbor most of the vegetation diversity of forests and are often sensitive to changes in climate and atmospheric deposition of nitrogen (N). As temperature increases from human-caused climate change and soils recover from long term atmospheric deposition of N and sulfur (S), it is unclear how these important ecosystem components will respond. We used the newly developed US-PROPS model - based on species response functions for over 1,500 species - to evaluate the potential impacts of atmospheric N deposition and climate change on species occurrence probability for a case study in the forested ecosystems of the Great Smoky Mountains National Park (GRSM), an iconic park in the southeastern United States. We evaluated six future scenarios from various combinations of two potential recoveries of soil pH (no change, +0.5 pH units) and three climate futures (no change, +1.5, +3.0 deg C). Species critical loads (CLs) of N deposition and projected responses for each scenario were determined. Critical loads were estimated to be low (< 2 kg N/ha/yr) to protect all species under current and expected future conditions across broad regions of GRSM and these CLs were exceeded at large spatial extents among scenarios. Northern hardwood, yellow pine, and chestnut oak forests were among the most N-sensitive vegetation map classes found within GRSM. Potential future air temperature conditions generally led to decreases in the maximum occurrence probability for species. Therefore, CLs were considered "unattainable" in these situations because the specified level of protection used for CL determination (i.e., maximum occurrence probability under ambient conditions) was not attainable. Although some species showed decreases in maximum occurrence probability with simulated increases in soil pH, most species were favored by increased pH. The importance of our study is rooted in the methodology described here for establishing regional CLs and for evaluating future conditions, which is transferable to other national parks in the U.S. and in Europe where the original PROPS model was developed.

Species distribution models: Administrative boundary centroid occurrences require careful interpretation

Describing and understanding species distributions and the factors driving them is fundamental to ecology and biogeography. Species distribution models (SDMs) allow one to investigate objectives of identifying ecologically important factors to the distribution, estimating species-environment responses, predicting the probability of species occurrence, and predicting species presence or absence. Mosquito occurrence records used in SDMs are often imprecise and represented as a centroid of a geopolitical/administrative boundary. Using a virtual species, we investigated the effect of centroids on SDMs and determined which methodology was best suited to provide accurate and applicable conclusions for each of the objectives. We compared 12 distinct algorithms, four levels of pseudo-absences, and three predictor sets to determine the optimal SDM methodology for each objective. The ability of methodology considerations to account for the effects of centroids varied for each objective. Ecologically important predictors were misidentified but could be best approximated by generalized additive models with 10,000 pseudo-absences. Response curves only captured the expected positive or negative trends. Centroids limited SDMs’ ability to differentiate expected probabilities, resulting in overprediction of high probability areas. Response curves and occurrence probabilities were best estimated by generalized boosting regression models. Species presence was largely over-estimated within southern regions, but underpredicted in northern regions, and was best estimated by weighted mean ensembles. Overall, generalized boosting regression methods and (weighted) mean ensembles provided the most reliable conclusions across all four objectives. Further, the most reliable conclusions were consistently observed with equal pseudo-absences when considered with the removal of low-contributing predictors, except for predictor identification.

Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data

Understanding the spatiotemporal distributions of migratory marine species at marine renewable energy sites is a crucial step towards assessing the potential impacts of tidal stream turbines and related infrastructure upon these species. However, the dynamic marine conditions that make tidal channels attractive for marine renewable power development also make it difficult to identify and follow species of marine fishes with existing technologies such as hydroacoustics and optical cameras. Acoustic telemetry can resolve some of these problems. Acoustic tags provide unique individual ID codes at an ultrasonic frequency, which are then detected and recorded by acoustic receivers deployed in the area of interest. By matching detection locations of fish species with environmental conditions at proposed sites for tidal energy infrastructure, species distribution models can be developed to predict the probability of species occurrence at sites of current and planned tidal power development. This information can be used to develop statistically robust encounter rate models to aid in quantifying the risk of tidal power development to migratory fish species. We used this approach to develop a predictive model of striped bass (Morone saxatilis) distribution within Minas Passage in the upper Bay of Fundy, Nova Scotia. Model results suggested increased probability of striped bass presence in Minas Passage during late ebb tide conditions and at relatively high water temperatures. We demonstrate the potential utility of species distribution modeling of acoustic tag detections in predicting interactions with renewable energy infrastructure, and show the importance of physical oceanographic variables influencing species distributions in a highly dynamic marine environment.

The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia.

Invasive species are now considered the second biggest threat for biodiversity and have adverse environmental, economic and social impacts. Understanding its spatial distribution and dynamics is crucial for the development of tools for large-scale mapping, monitoring and management. The aim of this study was to predict the distribution of invasive Fallopia taxa in Slovakia and to identify the most important predictors of spreading of these species. We designed models of species distribution for invasive species of Fallopia—Fallopia japonica—Japanese knotweed, Fallopia sachalinensis—Sakhalin knotweed and their hybrid Fallopia × bohemica—Czech knotweed. We designed 12 models—generalized linear model (GLM), generalized additive model (GAM), classification and regression trees (CART), boosted regression trees (BRT), multivariate adaptive regression spline (MARS), random forests (RF), support vector machine (SVM), artificial neural networks (ANN), maximum entropy (Maxent), penalized maximum likelihood GLM (GLMNET), domain, and radial basis function network (RBF). The accuracy of the models was evaluated using occurrence data for the presence and absence of species. The final simplified logistic regression model showed the three most important prediction variables lead by distances from roads and rails, then type of soil and distances from water bodies. The probability of invasive Fallopia species occurrence was evaluated using Pearson’s chi-squared test (χ). It significantly decreases with increasing distance from transport lines (χ = 118.85, p < 0.001) and depends on soil type (χ = 49.56, p < 0.001) and the distance from the water, where increasing the distance decrease the probability (χ = 8.95, p = 0.003).

Spatial and Temporal Variations of Cockle (Cerastoderma spp.) Populations in Two Portuguese Estuarine Systems With Low Directed Fishing Pressure

Cockles are amongst the most exploited bivalve species in Portugal, playing an important ecological and socioeconomic role in coastal ecosystems. Two sympatric species of cockles, Cerastoderma edule (Linnaeus, 1758) and Cerastoderma glaucum (Bruguière, 1789–1792) may co-occur in estuaries and coastal lagoons in mixed populations along the European Atlantic coast, namely in Portugal, France and the United Kingdom. The increasing importance of shellfish harvesting in Portugal requires a good knowledge of cockle stocks and temporal variability in stock levels to better inform sustainable management practices. Therefore, this study aimed at assessing spatial and temporal variations in cockle populations in two Portuguese estuarine systems where the species are exploited at low levels. Sampling was carried out using a clam dredge, covering the entire potential area of occurrence of cockles in the Tagus and Sado estuaries at around the same time of the year in each of the three sampling years (2015, 2018, and 2019). The abundance, spatial distribution and population structure of cockles were examined at each system. Moreover, several water and sediment parameters were measured to understand the influence of environmental conditions on the spatial distribution and abundance of cockles. The results obtained showed that cockles occur mostly in the intermediate areas of both estuarine systems and are more abundant in the Tagus estuary. Depth, average sediment grain size and the species Ruditapes philippinarum were the factors that better explained the probability of species occurrence. The population structure analysis indicated that natural mortality is constraining the cockle communities given the low abundance of adult individuals with marketable size in both estuaries. This study highlights the need for appropriate management measures to ensure the sustainability of these bivalve population stocks that have significant socioeconomic importance for local populations.

Unraveling a century of global change impacts on winter bird distributions in the eastern United States.

One of the most pressing questions in ecology and conservation centers on disentangling the relative impacts of concurrent global change drivers, climate and land-use/land-cover (LULC), on biodiversity. Yet studies that evaluate the effects of both drivers on species' winter distributions remain scarce, hampering our ability to develop full-annual-cycle conservation strategies. Additionally, understanding how groups of species differentially respond to climate versus LULC change is vital for efforts to enhance bird community resilience to future environmental change. We analyzed long-term changes in winter occurrence of 89species across nine bird groups over a 90-year period within the eastern United States using Audubon Christmas Bird Count (CBC) data. We estimated variation in occurrence probability of each group as a function of spatial and temporal variation in winter climate (minimum temperature, cumulative precipitation) and LULC (proportion of group-specific and anthropogenic habitats within CBC circle). We reveal that spatial variation in bird occurrence probability was consistently explained by climate across all nine species groups. Conversely, LULC change explained more than twice the temporal variation (i.e., decadal changes) in bird occurrence probability than climate change on average across groups. This pattern was largely driven by habitat-constrained species (e.g., grassland birds, waterbirds), whereas decadal changes in occurrence probabilities of habitat-unconstrained species (e.g., forest passerines, mixed habitat birds) were equally explained by both climate and LULC changes over the last century. We conclude that climate has generally governed the winter occurrence of avifauna in space and time, while LULC change has played a pivotal role in driving distributional dynamics of species with limited and declining habitat availability. Effective land management will be critical for improving species' resilience to climate change, especially during a season of relative resource scarcity and critical energetic trade-offs.

Functional Responses of Bird Assemblages to Land-Use Change in the Colombian Llanos Region

Land-use change in the Colombian Llanos due to agro-industrial expansion affects biodiversity. This change alters species occurrence probability, consequently impacting species’ composition. For some species, the occurence probability increases with land-use changes, while it stays unchanged or decreases for others. This interspecific variation in the response to land-use change may be mediated by functional traits, among other factors. We investigated response functional traits to land-use changes and their influence on the occurrence probability of bird species in the Colombian Orinoquia region. We compiled data for 13 morphological and life-history traits of 364 species recorded in forests, savannas, rice fields, palm oil crops, and livestock pastures in the piedmont and flooded savanna landscapes. We used a novel framework to identify response functional traits (i.e., traits with a significant effect on occurrence probability) through multiple statistical tests. We used random forest models to identify response functional traits to land-use change for pairwise comparisons of natural vs. agricultural land use types. For the functional traits, we estimated the influence of their states as trait attributes on species’ responses to land-use changes. We identified functional groups based on hierarchical clustering analysis. Functional groups corresponded to different levels of response, that is, different changes in probability occurrence. Land-use changes altered the multidimensional space of bird traits (i.e., functional diversity), implying modifications in species' composition, functional redundancy, and functional group turnover. Functional traits were similar for random forest classifications of the same natural cover but differed among landscapes. In the piedmont forests, social behavior—migratory status—was a functional trait combination common to all classifications, while foraging behavior-nest location trait combination was common to all forests scenarios in flooded savannas landscape classifications. Migratory status was a functional trait for all savanna classifications. Functional groups described the impacts of land-use changes on bird assemblages. Identification and characterization of these groups using trait attributes can help predict species' responses to land-use changes and guide conservation efforts toward groups with decreased occurrence probability, including recommendations for agricultural practices that can reduce impacts on the Orinoquia biodiversity.

Back-and-forth shifts in habitat selection by giant pandas over the past two decades in the Daxiangling Mountains, southwestern China

Habitat selection is a complex process, and animals may change their habitat selection over time. However, we have a poor understanding of temporal variation in habitat selection of endangered giant pandas (Ailuropoda melanoleuca), which has limited the development of conservation strategies for them. This study examined giant panda habitat selection in three stages of the past two decades (2001–2002, 2011–2012, and 2019–2020) in the Daxiangling Mountains, southwestern China. We applied the resource selection function to estimate the probability of species occurrence with five limiting habitat factors, i.e., elevation, slope, aspect, tree diameter at breast height, and bamboo cover. We found that giant pandas consistently selected for south- and west-facing slopes with mid-elevations and large trees in the three stages. However, there were shifts back-and-forth in the choice between higher and lower elevations and between higher and lower bamboo cover across the three stages. We suggest that the giant panda habitat selection had important consequences for gaining the high availability of palatable bamboo and good shelters. The back-and-forth shifts might be the result of changes in the type, spatial distribution, and intensity of human disturbance. Our study provides insights for improving our understanding and approaches for quantifying temporal variation in habitat selection in giant pandas and other species, suggesting that adaptive conservation and management in response to changing environments is needed.

Mismatch between species distribution and climatic niche optima in relation to functional traits

Forecasts of climate change impacts on biodiversity often assume that the current geographical distributions of species match their niche optima. However, empirical evidence has challenged this assumption, suggesting a mismatch. We examine whether the mismatch is related to functional traits along temperature or precipitation gradients. The observed distributions of 32 tree species in northeast China were evaluated to test this mismatch. Bayesian models were used to estimate the climatic niche optima, i.e. the habitats where the highest species growth and density can be expected. The mismatch is defined as the difference between the actual species occurrence in an assumed niche optimum and the habitat with the highest probability of species occurrence. Species’ functional traits were used to explore the mechanisms that may have caused the mismatches. Contrasting these climatic niche optima with the observed species distributions, we found that the distribution–niche optima mismatch had high variability among species based on temperature and precipitation gradients. However, these mismatches depended on functional traits associated with competition and migration lags only in temperature gradients. We conclude that more relevant research is needed in the future to quantify the mismatch between species distribution and climatic niche optima, which may be crucial for future designs of forested landscapes, species conservation and dynamic forecasting of biodiversity under expected climate change.

Evaluating the potential of urban areas for bat conservation with citizen science data

Global change, including urbanisation, threatens many of the >1400 bat species. Nevertheless, certain areas within highly urbanised cities may be suitable to harbour bat populations. Thus, managing urban habitats could contribute to bat conservation. Here, we wanted to establish evidence-based recommendations on how to improve urban spaces for the protection of bats. In a team effort with >200 citizen scientists, we recorded bat vocalisations up to six times over the course of 2 years at each of 600 predefined sites in the Berlin metropolitan area. For each species we identified the preferred and non-preferred landscape features. Our results show that artificial light at night (ALAN) had a negative impact on all species. For soprano pipistrelles and mouse-eared bats ALAN had the largest effect sizes among all environmental predictors. Canopy cover and open water were especially important for bat species that forage along vegetation edges and for trawling bats, respectively. Occurrence probability of species foraging in open space decreased with increasing distance to water bodies. On a larger scale, impervious surfaces tended to have positive effects on some species that are specialised on foraging along edge structures. Our study constitutes an important contribution to the growing body of literature showing that despite the many negative impacts of urbanisation on wildlife, urban environments can harbour bat populations if certain conditions are met, such as access to vegetation and water bodies and low levels of ALAN. Our findings are of high relevance for urban planners and conservationists, as they allow inferences on how to manage urban spaces in a bat-friendly way. We recommend limiting ALAN to the minimum necessary and maintaining and creating uninterrupted vegetated corridors between areas with high levels of canopy cover and water bodies, in which ALAN should be entirely avoided.

Testing the Influence of Seascape Connectivity on Marine-Based Species Distribution Models

Species distribution models (SDMs) are commonly used in ecology to predict species occurrence probability and how species are geographically distributed. Here, we propose innovative predictive factors to efficiently integrate information on connectivity into SDMs, a key element of population dynamics strongly influencing how species are distributed across seascapes. We also quantify the influence of species-specific connectivity estimates (i.e., larval dispersal vs. adult movement) on the marine-based SDMs outcomes. For illustration, seascape connectivity was modeled for two common, yet contrasting, marine species occurring in southeast Australian waters, the purple sea urchin, Heliocidaris erythrogramma, and the Australasian snapper, Chrysophrys auratus. Our models illustrate how different species-specific larval dispersal and adult movement can be efficiently accommodated. We used network-based centrality metrics to compute patch-level importance values and include these metrics in the group of predictors of correlative SDMs. We employed boosted regression trees (BRT) to fit our models, calculating the predictive performance, comparing spatial predictions and evaluating the relative influence of connectivity-based metrics among other predictors. Network-based metrics provide a flexible tool to quantify seascape connectivity that can be efficiently incorporated into SDMs. Connectivity across larval and adult stages was found to contribute to SDMs predictions and model performance was not negatively influenced from including these connectivity measures. Degree centrality, quantifying incoming and outgoing connections with habitat patches, was the most influential centrality metric. Pairwise interactions between predictors revealed that the species were predominantly found around hubs of connectivity and in warm, high-oxygenated, shallow waters. Additional research is needed to quantify the complex role that habitat network structure and temporal dynamics may have on SDM spatial predictions and explanatory power.