Biology subjects

Caplat, P.

Publications and source records attributed to Caplat, P..

4 recordsLinked to original sources

bioRxiv2024

Warm or bright - temperature and light microhabitat use in insect pollinators

O_LIEnvironmental heterogeneity in forest understories creates microhabitat niches that differ both spatially and temporally in light intensity and temperature. Do animal communities segregate in relation to these niche dimensions and can this be explained by functional traits? Answering these questions is particularly important for insect pollinators as they play a critical role in maintaining flowering plant biodiversity. C_LIO_LIBumblebees are essential pollinators of high altitude/latitude ecosystems and are particularly sensitive to climate change. In early spring, they forage on bilberry, a keystone species in heterogeneous habitats - hemi- boreal forests. We capitalized on these conditions to study species-specific selection of foraging niches in relation to abiotic conditions. C_LIO_LIWe combined full-day monitoring of bumblebee communities foraging in bilberry-dominated forests with joint species distribution modelling, which showed that temperature conditioned species occurrence, while light intensity explained species abundance. The inclusion of functional traits did not improve the overall explanatory and predictive power of the models. C_LIO_LIOur results suggest that temperature acts as a first filter of the local species pool and that species, once present, partition along a light intensity gradient. This study confirms and extends upon previous findings that microhabitat partitioning may act a mechanism underpinning bumblebee coexistence. It highlights the importance of focusing on micro-scales when studying how species interact with their environment, as this could, for example help improve our ability to predict consequences of global changes. C_LI

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↗