Biology subjects

White, H. J.

Publications and source records attributed to White, H. J..

5 recordsLinked to original sources

bioRxiv2025

Expert-led priorities for a response diversity research agenda in ecology

Response diversity aims to capture and explain the variation in ecological responses to environmental change. Response diversity is expected to drive ecological stability since a wider variety of responses to one or more environmental factors should stabilise fluctuations of ecosystem functions. However, uptake of empirical response diversity research has been slow. Here we assess current thinking around response diversity by conducting a targeted expert survey of response diversity researchers. Our survey revealed that one barrier to a unified research agenda on response diversity is the lack of agreement among respondents on the definition of response diversity, and to which dimension(s) of ecological stability response diversity might relate. When asked to select the temporal, spatial, and biological scales at which response diversity may be most relevant for ecological stability, respondents chose a wide range of scales indicating differences in how experts view response diversitys stabilising effect. Respondents considered studies incorporating both biotic interactions and abiotic environmental responses to be especially challenging, as were those thinking about responses to multiple environmental changes simultaneously. Moreover, respondents thought inconsistencies in the definitions of, and methods for measuring, response diversity were a major challenge facing the field. Despite these barriers, the survey revealed a desire for globally coordinated research efforts on response diversity in the form of syntheses, workshops, and distributed experiments, but that a standardised response diversity metric across diverse use-cases could be too restrictive. Our findings suggest we can shift response diversity from a loose collection of conceptual studies and inconsistent empirical applications towards a quantitative and coordinated research programme mechanistically linking biodiversity and ecological stability. As such, we are launching the Response Diversity Network--a research community interested in the science and application of response diversity-- whose activities we hope will benefit both individual studies of response diversity and globally coordinated research efforts.

ecology↗

bioRxiv2025

Drivers of individual plant species contributions to β-diversity are scale-dependent

Species introductions and native extirpations are driving biotic homogenisation in plant communities by reducing {beta}-diversity. Individual species vary in their contributions to {beta}-diversity (Species Contribution to {beta}-Diversity; species-{beta}), yet our understanding of how species characteristics shape these contributions remains limited. Additionally, although the ecological processes influencing {beta}-diversity are known to vary with spatial scale, we lack understanding of how species contributions, or their underlying determinants, change across scales. Here, we modelled how plant functional traits, phylogenetic relatedness, and introduction status influence their contributions to {beta}-diversity using plant community data from 429 plots surveyed from 2017-2023 across three nested spatial scales (up to 1 km2) in nine sites spanning four countries. We extended the analysis to broader spatial extents (100 km2 to the entire UK) using GBIF occurrence data. We found that functional traits associated with competitive ability influenced species-{beta}, but the direction and strength of their effects varied with scale. Likewise, phylogenetic novelty increased species-{beta} at small scales but reduced it at larger ones. After accounting for traits and phylogeny, introduced species consistently contributed less to {beta}-diversity than native species--especially at broader spatial extents. These results demonstrate that species ecological and evolutionary characteristics shape their contributions to {beta}-diversity, but that these effects are scale-dependent. Our findings highlight the importance of scale-explicit approaches in understanding both the determinants of {beta}-diversity, and how we can combat its loss to mitigate biotic homogenisation.

ecology↗

bioRxiv2023

Weak interactions cause poor performance of common network inference models

O_LINetwork inference models have been widely applied in ecological, genetic and social studies to infer unknown interactions. However, little is known about how well the models perform and whether they produce reliable results when confronted with networks where weak interactions predominate and for different amounts of data. This is an important consideration as empirical interaction strengths are commonly skewed towards weaker interactions, which is especially relevant in ecological networks, and a number of studies suggest the importance of weak interactions for ensuring the dynamic stability of a system. C_LIO_LIHere we investigate four commonly used network methods (Bayesian Networks, Graphical Gaussian Models, L1-regularised regression with the least absolute shrinkage and selection operator, and Sparse Bayesian Regression) and employ network simulations with different interaction strengths to assess their accuracy and reliability. C_LIO_LIThe results show poor performance, in terms of the ability to discriminate between existing relationships and no relationships, in the presence of weak interactions, for all the selected network inference methods. C_LIO_LIOur findings suggest that though these models have some promise for network inference with networks that consist of medium or strong interactions and larger amounts of data, data with weak interactions does not provide enough information for the models to reliably identify interactions. Therefore, networks inferred from data of that type should be interpreted with caution. C_LI

ecology↗

bioRxiv2022

Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

AimSoil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. LocationIsland of Ireland. MethodsWe tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. ResultsUsing spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main ConclusionsSpatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.

ecology↗

bioRxiv2020

Data quantity is more important than its spatial bias for predictive species distribution modelling

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of 1) spatial bias in training data, 2) sample size (the average number of observations per species), and 3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but only when the bias was relatively strong. Sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

ecology↗