Biology subjects

Broseth, H.

Publications and source records attributed to Broseth, H..

5 recordsLinked to original sources

bioRxiv2023

Including biotic interactions in species distribution models improves the understanding of species niche: a case of study with the brown bear in Europe

Biotic interactions are expected to influence species responses to climate change, but they are usually not included when predicting future range shifts. We assessed the importance of biotic interactions to understand future consequences of climate and land use change for biodiversity using as a model system the brown bear (Ursus arctos) in Europe. By including biotic interactions using the spatial variation of energy contribution and habitat models of each food species, we showed that the use of biotic factors considerably improves our understanding of the distribution of brown bears. Predicted future range shifts, which included changes in the distribution of food species, varied greatly when considering various scenarios of change in biotic factors, warning about future indirect climate change effects. Our study confirmed that advancing our understanding of ecological networks of species interactions will improve future scenarios of biodiversity change, which is key for conserving biodiversity and ecosystem services.

ecology↗

bioRxiv2022

Wolverine density distribution reflects past persecution and current management in Scandinavia

After centuries of intense persecution, several large carnivore species in Europe and North America have experienced a rebound. Todays spatial configuration of large carnivore populations has likely arisen from the interplay between their ecological traits and current environmental conditions, but also from their history of persecution and protection. Yet, due to the challenge of studying population-level phenomena, we are rarely able to disentangle and quantify the influence of past and present factors driving the spatial distribution and density of these controversial species. Using spatial capture-recapture models and a data set of 742 genetically identified wolverines Gulo gulo collected over [1/2] million km2 across their entire range in Norway and Sweden, we identify landscape-level factors explaining the current population density of wolverines in the Scandinavian Peninsula. Distance from the relic range along the Swedish-Norwegian border, where the wolverine population survived a long history of persecution, remains a key determinant of wolverine density today. However, regional differences in management and environmental conditions also played an important role in shaping spatial patterns in present-day wolverine density. Specifically, we found evidence of slower recolonization in areas that had set lower wolverine population goals in terms of the desired number of annual reproductions. Management of transboundary large carnivore populations at biologically relevant scales may be inhibited by administrative fragmentation. Yet, as our study shows, population-level monitoring is an achievable prerequisite for a comprehensive understanding of the distribution and density of large carnivores across an increasingly anthropogenic landscape.

ecology↗

bioRxiv2020

Instrumented individuals are not representative of the population. Survival of wolverines with and without GPS collars

Results from biologging studies are often scaled up to population-level inferences and this begs the question: Are instrumented animals representative of the population given the potential bias in individual selectivity, the influence of capture, handling and wearing bio-loggers? The answer is elusive due to the challenges of obtaining comparable data from individuals without bio-loggers. Using non-invasive genetic data of a large carnivore, the wolverine (Gulo gulo) in Scandinavia, and an open-population spatial capture-recapture model, we were able to estimate and compare survival rates of individuals fitted with GPS collars versus individuals that had never been captured and collared. We found that mortality of GPS collared individuals was 47% lower compared to individuals without GPS collars. While instrumented and non-instrumented wolverines had similar risks of dying from legal culling, GPS collared individuals displayed lower mortality rates due to causes other than legal culling. Reports of positive effects of biologging on the fitness of individuals are rather uncommon and we argue that the most likely explanation is that GPS-collars could shield animals from poaching. Our results highlight the challenges of drawing population level inferences for large carnivores when using data from an instrumented sample of the population.

ecology↗

bioRxiv2020

Efficient Estimation of Large-Scale Spatial Capture-Recapture Models

Capture-recapture methods are a common tool in ecological statistics, which have been extended to spatial capture-recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. We provide a cumulative series of methods that yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples. These include removing unnecessary computations, integrating out latent states, vectorizing declarations, and restricting calculations to the locality of individuals. Our approaches leverage the flexibility provided by the nimble R package. In our first example, we demonstrate an improvement in MCMC efficiency (the rate of generating effectively independent posterior samples) by a factor of 100. In our second example, we reduce the computing time required to generate 10,000 posterior samples from 4.5 hours down to five minutes, and realize an increase in MCMC efficiency by a factor of 25. We also explain how these approaches can be applied generally to other spatially-indexed hierarchical models. R code is provided for all examples, as well as an executable web-appendix.

ecology↗

bioRxiv2019

Estimating abundance with interruptions in data collection using open population spatial capture-recapture models

O_LIThe estimation of population size remains one of the primary goals and challenges in ecology and provides a basis for debate and policy in wildlife management. Despite the development of efficient non-invasive sampling methods and robust statistical tools to estimate abundance, maintenance of field sampling is still subject to economic and logistic constraints. These can result in intentional or unintentional interruptions in sampling and cause gaps in data time series, posing a challenge to abundance estimation, and ultimately conservation and management decisions.\nC_LIO_LIWe applied an open population spatial capture-recapture (OPSCR) model to simulations and a real case study to test the reliability of abundance inferences models to interruption in data collection. Using individual detections occurring over consecutive sampling occasions, OPSCR models allow the estimation of abundance from individual detection data while accounting for lack of demographic and geographic closure between occasions. First, we simulated sampling data with interruptions in field sampling of different lengths and timing. We checked the performance of an OPSCR model in deriving abundance for species with slow and intermediate life history strategies. Finally, we introduced artificial sampling interruptions of various magnitudes and timing to a five-year non-invasive monitoring data set of wolverines (Gulo gulo) in Norway and quantified the consequences for OPSCR model predictions.\nC_LIO_LIInferences from OPSCR models were reliable even with temporal interruptions in monitoring. Interruption did not cause any systematic bias, but increased uncertainty. Interruptions occurring at occasions towards the beginning and the end of the sampling caused higher uncertainty. The loss in precision was more severe for species with a faster life history strategy.\nC_LIO_LIWe provide a reliable framework to estimate abundance even in the presence of sampling interruptions. OPSCR allows monitoring studies to provide contiguous abundance estimates to managers, stakeholders, and policy makers even when data are non-contiguous. OPSCR models do not only help cope with unintentional interruptions during sampling but also offer opportunities for using intentional sampling interruptions during the design of cost-effective population surveys.\nC_LI

ecology↗