Biology subjects

Beninde, J.

Publications and source records attributed to Beninde, J..

3 recordsLinked to original sources

bioRxiv2025

Disparity in the Distribution of Suitable Habitat: A Metric for Capturing and Tracking Responses to Urbanized Landscapes

Global species observations from community science platforms offer an unprecedented opportunity to analyze large-scale biodiversity patterns. This holds great promise for developing data-driven metrics to measure species responses to landscape-level environmental changes, essential for ameliorating the ongoing biodiversity crisis. A primary cause of declines in local, native species is the expansion and intensification of urbanized landscapes. A species response to urbanization can remain consistent across spatial and temporal scales. However, city-specific factors, such as land-use dynamics or local biotic interactions, can profoundly influence species and cause shifts in responses. This underscores the importance of measuring responses to urbanized landscapes at the local population level in a way that facilitates spatial and temporal comparisons. I propose utilizing the receiver operating characteristic (ROC) and its area under the curve (AUC), a statistical classifier, in a novel way for this purpose. Specifically, the disparity in suitable habitat between urban and adjacent non-urban areas is summarized by a single metric, HDURBAN, which facilitates population-level comparisons across time and space. I apply it to geographic predictions of the realized niche of 1,023 species from a highly urbanized landscape in California, USA. Additionally, modeling virtual species for each of the urban response types introduced by Blairs seminal 1996 study provides place-based, expected HDURBAN scores for urban avoider, urban utilizer, and urban dweller species. Validation efforts demonstrate accurate predictions of expected urban responses of real species in three scenarios. First, following a route-of-introduction hypothesis, non-native species exhibit significantly higher HDURBAN than native species. Second, assessing the robustness of urban responses in space and time, HDURBAN is significantly correlated to the urban response of the same 38 bird species from Blairs study, conducted 450 km apart and 25 years earlier. Third, testing for sensitivity to temporal shifts within species, HDURBAN decreases significantly in a species displaced from urban areas due to negative interactions with a recently introduced, congeneric species. In summary, HDURBAN is a versatile metric that provides a data-driven way to compare species responses, supporting future research into the patterns and processes underlying spatial and temporal variation in responses to urbanized landscapes.

ecology↗

bioRxiv2025

Species distribution modeling for conservation science: new predictor layers, reproducible code, and an evaluation of California protected areas

AimOur study provides foundational resources for future SDMing: methods for generating fine-scale, equal-area predictor datasets and best-practice SDM guidelines. We also provide reproducible code to streamline their implementation. LocationSouthwestern North America MethodsUsing over 215,000 research-grade iNaturalist occurrence records for 127 species of conservation concern or scientific interest in California and surrounding area, we quantified and compared SDM performance between two predictor datasets that differ in their source of bioclimatic data, spatial resolution, and coordinate reference system: one generated using ClimateNA software (resolution = 300 x 300 m; NAD83/California Albers) and the other using existing WorldClim data (varying resolution = [~]669-797 x 926 m; WGS84). We also compared two modeling algorithms (MaxEnt vs Random Forests), and two background point selection strategies (random points vs weighted points accounting for sampling effort). As an example application, we used SDM predictions to evaluate the conservation value of different protected area types within California. ResultsClimateNA outperformed WorldClim for 94% of species, Random Forests outperformed MaxEnt for 87%, and random background points outperformed weighted background points for 100%. All differences were statistically significant. Together, the ClimateNA dataset, Random Forests, and random background points achieved highest performance for 86% of species. Using this best-performing set of models, we found that regional parks, county parks, state beaches, and open spaces in California were highest in multi-species suitability, while larger protected areas, such as national parks and national forests, generally exhibited surprisingly low suitability. Substantial spatial biases intrinsic to SDMing with unprojected predictor datasets (e.g., WGS84) are described, along with clear solutions using equal-area predictor datasets. Main conclusionsConsiderable disparity was observed among the performance of common SDM methods. This study highlights the importance of fine-scale, equal-area predictor datasets and best-practice guidelines, and demonstrates how SDMs can provide critical insights into protected area planning.

ecology↗

bioRxiv2021

Using empirical datasets to quantify uncertainty in inferences of landscape genetic resistance due to variation of individual-based genetic distance metrics

O_LIEstimates of gene flow are commonly based on inferences of landscape resistance in ecological and evolutionary research and they frequently inform decision-making processes in conservation management. It is therefore imperative that inferences of a landscape factors relevance and its resistance are robust across approaches and reflect real-world gene flow instead of methodological artefacts. Here, we tested the impact of 160 different individual-based pairwise genetic metrics on consistency of landscape genetic inferences. C_LIO_LIWe used three empirical datasets that adopted individual-based sampling schemes and varied in scale (35-25,000 km2) and total number of samples (184-790) and comprise the wild boar, Sus scrofa, the red fox, Vulpes vulpes and the common wall lizard, Podarcis muralis. We made use of a machine-learning algorithm implemented in RO_SCPLOWESISTANCEC_SCPLOWGA to optimally fit resistances of landscape factors to genetic distance metrics and ranked their importance. Employed for nine landscape factors this resulted in 4,320 unique combinations of dataset, landscape factor and genetic distance metric, which provides the basis for quantifying uncertainty in inferences of landscape resistance. C_LIO_LIOur results demonstrate that there are clear differences in Akaike information criteria (AICc)-based model support and marginal R2-based model fit between different genetic distance metrics. Metrics based on between 1-10 axes of eigenvector-based multivariate analyses (Factorial correspondence analysis, FCA; Principal component analysis, PCA) outperformed more widely used metrics, including the proportion of shared alleles (DPS), with AICc and marginal R2 values often an order of magnitude greater in the former. Across datasets, inferences of the directionality of a landscape factors influence on gene flow, e.g. facilitating or impeding it, changed across different genetic distance metrics. The directionality of the inferred resistance was largely consistent when considering metrics based on between 1-10 FCA/PCA axes. C_LIO_LIInferences of landscape genetic resistance need to be corroborated using calculations of multiple individual-based pairwise genetic distance metrics. Our results call for the adoption of eigenvector-based quantifications of pairwise genetic distances. Specifically, a preliminary step of analysis should be incorporated, which explores model ranks across genetic distance metrics derived from FCA and PCA, and, contrary to findings of a simulation study, we demonstrate that it suffices to quantify these distances spanning the first ten axes only. C_LI

ecology↗