Biology subjects

Reineking, B.

Publications and source records attributed to Reineking, B..

4 recordsLinked to original sources

bioRxiv2024

Flexible movement kernel estimation in habitat selection analyses with generalized additive models

O_LIHabitat selection analysis includes resource selection analysis (RSA) and step selection analysis (SSA). These frameworks are used in order to understand space use of animals. Particularly, the SSA approach specifies the space availability of sequential locations through a movement kernel. This movement kernel is typically defined as the product of independent parametric distributions of step lengths (SLs) and turning angles (TAs). However, this assumption may not always be plausible for real data where short SLs are often correlated with large TAs and vice versa. C_LIO_LIThe objective of this paper is to relax the need for parametric distributions using generalized additive models (GAMs) and the R-package mgcv, based on the work of Klappstein et al. (2024). For this, we propose to specify the movement kernel as a bivariate tensor product, rather than independent distributions of SLs and TAs. In addition, we account for residual spatial autocorrelation in this GAM-approach. C_LIO_LIUsing simulations, we show that the tensor product approach accurately estimates the underlying movement kernel and that the fixed effects of the model are not biased. In particular, if the data are simulated with a copula distribution for SL and TA, i.e. if the independence assumption for SL and TA does not hold, the GAM approach produces better estimates than the classical approach. In addition, including a bivariate tensor product in the model leads to a better uncertainty estimation of the model parameters and a higher predictive quality of the model. C_LIO_LIIncorporating a bivariate tensor product solves the problem of assuming parametric distributions and independence between SLs and TAs. This offers greater flexibility and makes the analysis of real data more reliable. C_LI

ecology↗

bioRxiv2023

Simulating animal space use from fitted integrated Step-Selection Functions (iSSF)

O_LIA standing challenge in the study of animal movement ecology is the capacity to predict where and when an individual animal might occur on the landscape, the so-called, Utilization Distribution (UD). Under certain assumptions, the steady-state UD can be predicted from a fitted exponential habitat selection function. However, these assumptions are rarely met. Furthermore, there are many applications that require the estimation of transient dynamics rather than steady-state UDs (e.g. when modeling migration or dispersal). Thus, there is a clear need for computational tools capable of predicting UDs based on observed animal movement data. C_LIO_LIIntegrated Step-Selection Analyses (iSSAs) are widely used to study habitat selection and movement of wild animals, and result in a fully parametrized individual-based model of animal movement, which we refer to as an integrated Step Selection Function (iSSF). An iSSF can be used to generate stochastic animal paths based on random draws from a series of Markovian redistribution kernels, each consisting of a selection-free, but possibly habitat-influenced, movement kernel and a movement-free selection function. The UD can be approximated by a sufficiently large set of such stochastic paths. C_LIO_LIHere, we present a set of functions in R to facilitate the simulation of animal space use from fitted iSSFs. Our goal is to provide a general purpose simulator that is easy to use and is part of an existing workflow for iSSAs (within the amt R package). C_LIO_LIWe demonstrate through a series of applications how the simulator can be used to address a variety of questions in applied movement ecology. By providing functions in amt and coded examples, we hope to encourage ecologists using iSSFs to explore their predictions and model goodness-of-fit using simulations, and to further explore mechanistic approaches to modeling landscape connectivity. C_LI

ecology↗

bioRxiv2022

Beyond mean fitness: demographic stochasticity and resilience matter at tree species climatic edges.

AimLinking local population dynamics and species distributions is critical to predicting the impacts of climate change. While many studies focus on the mean fitness of populations, theory shows that species distributions can be shaped by demographic stochasticity or population resilience. Here we examine how mean fitness (measured by invasion rate), demographic stochasticity, and resilience (measured by the ability to recover from disturbance) constrain populations at the edges compared to the climatic center. LocationEurope: Spain, France, Germany, Finland, and Sweden. PeriodForest inventory data used for fitting the models cover the period from 1985 to 2013. Major taxaDominant European tree species; Angiosperms and Gymnosperms. MethodsWe developed dynamic population models covering the entire life cycle of 25 European tree species with climatically dependent recruitment models fitted to forest inventory data. We then ran simulations using integral projection and individual-based models to test how invasion rates, risk of stochastic extinction, and ability to recover from stochastic disturbances differ between the center and edges of species climatic niches. ResultsResults varied among species, but in general, demographic constraints were stronger at warm edges and for species in harsher climates. Conversely, recovery was more limiting at cold edges. In addition, we found that for several species, constraints at the edges were due to demographic stochasticity and recovery capacity rather than mean fitness. Main conclusionOur results highlight that mean fitness is not the only mechanism at play at the edges; demographic stochasticity and population capacity to recover also matter for European tree species. To understand how climate change will drive species range shifts, future studies will need to analyse the interplay between population mean growth rate and stochastic demographic processes as well as disturbances.

ecology↗

bioRxiv2019

Local patch occupancy dynamics match the distribution of European trees at continental scale

Aim Processes driving current tree species distribution are still largely debated. Attempts to relate species distribution and population demography metrics have shown mixed results. In this context, we would like to test the hypotheses that the metapopulation processes of colonization and extinction are linked to species distribution models.Location Europe: Spain, France, Germany, Finland, and Sweden.Taxon Angiosperms and Gymnosperms.Methods For the 17 tree species analyzed we fitted species distribution model (SDM) relating environmental variables to presence absence data across Europe. Then using independent data from national forest inventories across Europe we tested whether colonization and extinction probabilities are related to occurrence probability estimated by the SDMs. Finally, we tested how colonization and extinction respectively drive probability of presence at the metapopulation equilibrium.Results We found that for most species at least one process (colonization/extinction) is related to the occurrence probability, but rarely both.Main conclusions Our study supports the view that metapopulation dynamics are partly related to SDM occurrence probability through one of the metapopulation probabilities. However these links are relatively weak and the metapopulation models tend to overestimate the occurrence probability. Our results call for caution in model extrapolating SDM models to metapopulation dynamics.Competing Interest StatementThe authors have declared no competing interest.View Full Text

ecology↗