Biology subjects

Renner, I.

Publications and source records attributed to Renner, I..

3 recordsLinked to original sources

bioRxiv2019

Classification of unlabelled observations in Species Distribution Modelling using Point Process Models.

1O_LISpecies distribution modelling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit species distribution models. However, the species observations used in species distribution models can have varying levels of quality and can have incomplete information, such as uncertain species identity.\nC_LIO_LIIn this paper, we develop two algorithms to reclassify observations with unknown species identities which simultaneously predict different species distributions using spatial point processes. We compare the performance of the different algorithms using different initializations and parameters with models fitted using only the observations with known species identity through simulations.\nC_LIO_LIWe show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that didnt use the misspecified data.\nC_LIO_LIThese models represent an helpful and promising tool for opportunistic surveys where misidentification happens or for the distribution of species newly separated in their taxonomy.\nC_LI

ecology↗

bioRxiv2019

Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalised likelihood maximisation

O_LIThe increase in availability of species data sets means that approaches to species distribution modelling that incorporate multiple data sets are in greater demand. Recent methodological developments in this area have led to combined likelihood approaches, in which a log-likelihood comprised of the sum of the log-likelihood components of each data source is maximised. Often, these approaches make use of at least one presence-only data set and use the log-likelihood of an inhomogeneous Poisson point process model in the combined likelihood construction. While these advancements have been shown to improve predictive performance, they do not currently address challenges in presence-only modelling such as checking and correcting for violations of the independence assumption of a Poisson point process model or more general challenges in species distribution modelling such as overfitting.\nC_LIO_LIIn this paper, we present an extension of the combined likelihood frame-work which accommodates alternative presence-only likelihoods in the presence of spatial dependence as well as lasso-type penalties to account for potential overfitting. We compare the proposed combined penalised likelihood approach to the standard combined likelihood approach via simulation and apply the method to modelling the distribution of the Eurasian lynx in the Jura Mountains in eastern France.\nC_LIO_LIThe simulations show that the proposed combined penalised likelihood approach has better predictive performance than the standard approach when spatial dependence is present in the data. The lynx analysis shows that the predicted maps vary significantly between the model fitted with the proposed combined penalised approach accounting for spatial dependence and the model fitted with the standard combined likelihood.\nC_LIO_LIThis work highlights the benefits of careful consideration of the presence-only components of the combined likelihood formulation, and allows greater flexibility and ability to accommodate real datasets.\nC_LI

ecology↗

bioRxiv2018

Identifying priority conservation areas for recovering large carnivores using citizen science data

Understanding the processes related to wildlife recoveries is not only essential in solving human - wildlife conflicts, but also for identifying priority conservation areas and in turn, for effective conservation planning. We used data from a large citizen science program to study the spatial processes related to the demographic and genetic recovery of brown bears in Greece and to identify new areas for their conservation. This was achieved by visually comparing our data with an estimation of the past distribution of brown bears in Greece and by using a Point Process Model to model habitat suitability, and then comparing our results with the current distribution of brown bear records and with that of protected areas. Our results indicate that in the last 15 years bears may have increased their range by as much as 100%, by occupying mainly anthropogenic landscapes and areas with suitable habitat that are currently not legally protected, thus creating a new conservation reality for the species in Greece. This development dictates the re-evaluation of the national management and conservation priorities for brown bears in Greece by focusing in establishing new protected areas that will safeguard their recovery. Our conservation approach is a swift and cheap way of identifying priority conservation areas, while gaining important insights on the spatial processes associated with population recoveries. It will help prioritize conservation actions for brown bears in Greece and may serve as a model conservation approach to countries facing financial and logistic constraints in the monitoring of local biodiversity or facing challenges in managing rapid population recoveries. Our conservation approach appeared also to be better suited to identifying priority areas for conservation in areas with recovering wildlife populations and may therefore be used as an \"early-warning\" conservation system.

ecology↗