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Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred. Excluding highly correlated variables does not affect Maxent model performance. Model transfer may lead to novel environment and collinearity shift, while both can negatively affect model performance.

Ecological niche modeling (ENM) is used widely to study species’ geographic distributions. ENM applications frequently involve transferring models calibrated with environmental data from one region to other regions or times that may include novel environmental conditions. When novel conditions are present, transferability implies extrapolation, whereas, in absence of such conditions, transferability is an interpolation step only. We evaluated transferability of models produced using 11 ENM algorithms from the perspective of interpolation and extrapolation in a virtual species framework. We defined fundamental niches and potential distributions of 16 virtual species distributed across Eurasia. To simulate real situations of incomplete understanding of species’ distribution or existing fundamental niche (environmental conditions suitable for the species contained in the study area; N* F ), we divided Eurasia into six regions and used 1–5 regions for model calibration and the rest for model evaluation. The models produced with the 11 ENM algorithms were evaluated in environmental space, to complement the traditional geographic evaluation of models. None of the algorithms accurately estimated the existing fundamental niche (N* F ) given one region in calibration, and model evaluation scores decreased as the novelty of the environments in the evaluation regions increased. Thus, we recommend quantifying environmental similarity between calibration and transfer regions prior to model transfer, providing an avenue for assessing uncertainty of model transferability. Different algorithms had different sensitivity to completeness of knowledge of N* F , with implications for algorithm selection. If the goal is to reconstruct fundamental niches, users should choose algorithms with limited extrapolation when N* F is well known, or choose algorithms with increased extrapolation when N* F is poorly known. Our assessment can inform applications of ecological niche modeling transference to anticipate species invasions into novel areas, disease emergence in new regions, and forecasts of species distributions under future climate conditions.

The structural heterogeneity of vegetation is a key factor for explaining animal diversity patterns at a local scale. Improvements in airborne light detection and ranging (lidar) technologies have enabled researchers to study forest 3D structure with increasing accuracy. Most structure–animal diversity work has focused on structural metrics derived from lidar returns from canopy and terrain features. Here, we built new lidar structural metrics based on the Leaf Area Density (LAD) at each vegetation height layer, and used these metrics to study how different aspects of forest structural heterogeneity explain variation in bird species richness. Our goals were to test: (1) whether LAD-based metrics better explained bird species richness compared to metrics based on the top of the canopy; and (2) if different aspects of structural heterogeneity had diverse effects on bird richness. We used discrete lidar data together with 61 breeding landbird points provided by the National Ecological Observatory Network at five forest sites of the eastern US. We used the lidar metrics as predictors of bird species richness and analyzed the shape of the response curves against each predictor. Metrics based on LAD measurements had better explanatory power (43% of variance explained) than those based on the variation of canopy heights (32% of variance explained). Dividing the forest plots into smaller grids allowed us to study the within-plot horizontal variation of the vertical heterogeneity, as well as to analyze how the vegetation density is horizontally distributed at each height layer. Bird species richness increased with horizontal heterogeneity, while vertical heterogeneity had negative effects, contrary to previous research. The increasing capabilities of lidar will allow researchers to characterize forest structure with higher detail. Our findings highlight the need for structure–animal diversity studies to incorporate metrics that are able to capture different aspects of forest 3D heterogeneity.

Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines. The authors evaluate the reproducibility of ecological niche modelling literature and provide a checklist of crucial items for more reproducible ecological niche models.

Human-lion conflict is one of the leading threats to lion populations and while livestock loss is a source of conflict, the degree to which livestock depredation is tolerated by people varies between regions and across cultures. Knowledge of local attitudes towards lions and identification of drivers of human-lion conflict can help formulate mitigation measures aimed at promoting coexistence of humans with lions. We assessed locals' attitudes towards lions in and around Gambella National Park and compared the findings with published data from Kafa Biosphere Reserve, both in western Ethiopia. We used household interviews to quantify livestock loss. We found that depredation was relatively low and that disease and theft were the top factors of livestock loss. Remarkably, however, tolerance of lions was lower around Gambella National Park than in Kafa Biosphere Reserve. Multivariate analysis revealed that education level, number of livestock per household, livestock loss due to depredation, and livestock loss due to theft were strong predictors of locals' attitude towards lion population growth and conservation. We show that the amount of livestock depredation alone is not sufficient to understand human-lion conflicts and we highlight the importance of accounting for cultural differences in lion conservation. The low cultural value of lions in the Gambella region corroborate the findings of our study. In combination with growing human population and land-use change pressures, low cultural value poses serious challenges to long-term lion conservation in the Gambella region. We recommend using Arnstein's ladder of participation in conservation education programs to move towards proactive involvement of locals in conservation.

We compared predictive success in two common algorithms for modeling species’ ecological niches, GARP and Maxent, in a situation that challenged the algorithms to be general – that is, to be able to predict the species’ distributions in broad unsampled regions, here termed transferability. The results were strikingly different between the two algorithms – Maxent models reconstructed the overall distributions of the species at low thresholds, but higher predictive levels of Maxent predictions reflected overfitting to the input data; GARP models, on the other hand, succeeded in anticipating most of the species’ distributional potential, at the cost of increased (apparent, at least) commission error. Receiver operating characteristic (ROC) tests were weak in discerning models able to predict into broad unsampled areas from those that were not. Such transferability is clearly a novel challenge for modeling algorithms, and requires different qualities than does predicting within densely sampled landscapes – in this case, Maxent was transferable only at very low thresholds, and biases and gaps in input data may frequently affect results based on higher Maxent thresholds, requiring careful interpretation of model results.

Mixed-species groups and aggregations are quite common and may provide substantial fitness-related benefits to group members. Individuals may benefit from the overall size of the mixed-species group or from the diversity of species present, or both. Here we exposed mixed-species flocks of songbirds (Carolina chickadees, Poecile carolinensis , tufted titmice, Baeolophus bicolor , and the satellite species attracted to these two species) to three different novel feeder experiments to assess the influence of mixed-species flock size and composition on ability to solve the feeder tasks. We also assessed the potential role of habitat density and traffic noise on birds’ ability to solve these tasks. We found that likelihood of solving a novel feeder task was associated with mixed-species flock size and composition, though the specific social factor involved depended on the particular species and on the novel feeder. We did not find an influence of habitat density or background traffic noise on likelihood of solving novel feeder tasks. Overall, our results reveal the importance of variation in mixed-species group size and diversity on foraging success in these songbirds.

Aim The Appalachian forests ecoregion in eastern North America supports a diverse and highly endemic temperate biota, which is potentially threatened by rapid climate change. We investigated possible outlooks for biodiversity in this biologically important ecoregion under future climate change. Location Appalachian forests ecoregion, USA. Methods We estimated distributional shifts for 258 forest‐dependent vertebrates of conservation concern in four major taxonomic groups (amphibians, birds, mammals and reptiles) under short‐ (2040) and long‐term (2080s) climate change using ecological niche modelling. We examined which groups are likely most vulnerable to climate change and identified places predicted to experience the greatest changes in their species assemblages and those predicted to support high species richness under various future scenarios. To assess species' vulnerability, we compared and regressed future projected range against present range estimates for each species. To evaluate which places could see greatest changes, we mapped species richness and turnover in each taxonomic group. Results A total of 30%–33% and 31%–38% of species were predicted to lose > 50% of area that is currently climatically suitable under short‐ and long‐term climate change, respectively. We expect climate change to impact amphibians and mammals more than birds and reptiles: a much larger fraction of amphibian and mammal (22%–48% in 2040; 29%–56% in 2080s) species were predicted to lose more than half of their present climatically suitable habitat area compared with birds and reptiles (1%–12% in 2040; 6%–12% in 2080s). While species were predicted to move northward and upslope assuming full dispersal, the Blue Ridge, Cumberland Plateau and southern Central Appalachians subregions are poised to remain as richness hotspots under the broad range of scenarios explored in this study. Main conclusions Our results highlighted species groups and locations that policymakers and conservation practitioners should emphasize in planning vertebrate conservation efforts in the Appalachians under future climate scenarios.

Human-caused changes to habitats like forestry practices and traffic noise can negatively influence antipredator and foraging behavior in animals. These behavior patterns are also frequently positively influenced by individuals being part of mixed-species groups. However, we know little about how such human-induced changes impact these behaviors in individuals of mixed-species groups. To address this gap, we examined the effects of mixed-species group composition, traffic noise, and vegetation density on antipredator and foraging behavior. We used feeders to attract mixed-species flocks of Carolina chickadees (Poecile carolinensis), tufted titmice (Baeolophus bicolor), and white-breasted nuthatches (Sitta carolinensis). Once we detected a flock at a feeder, we presented a Cooper’s hawk model and recorded flocks’ seed-taking and calling behaviors. Titmice avoided feeders more when hawk models were presented at sites with greater vegetation density. Nuthatches called more quickly with more conspecifics in their flocks, and they tended to take seed more quickly with greater diversity of species in their flocks. We did not detect the effects of physical or social environmental variables on chickadee behavior. Our results reveal individual sensitivity to environmental variation in contexts involving visual predator stimuli. More work is needed to investigate how various predator stimulus modalities affect antipredator behaviors of mixed-species flock members.

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.