Intensive management is frequently required in fenced wildlife areas to reduce deleterious effects of isolation. Decisions on how best to manage such wildlife are ideally informed by regular and reliable estimates of spatiotemporal fluctuations in population size and structure. However, even in small, fenced areas, it is difficult and costly to regularly monitor key species using advanced methods. This is particularly the case for large carnivores, which typically occur at low density and are elusive yet are central to management decision-making due to their top-down effects in ecosystems and attracting tourism. In this study, we aimed to provide robust estimates of population parameters for African lions () and use the data to inform a resource-efficient long-term monitoring programme. To achieve this, we used unstructured spatial sampling to collect data on lions in Pilanesberg National Park, a small (~550 km) fenced protected area in South Africa. We used Bayesian spatial capture-recapture models to estimate density, abundance, sex ratio and home range size of lions over the age of 1 year. Finally, to provide guidance on resource requirements for regular monitoring, we rarefied our empirical data set incrementally and analysed the subsets. Lion density was estimated to be 8.8 per 100 km (posterior SD = 0.6), which was lower than anticipated by park management. Sex ratio was estimated close to parity (0.9♀:1♂), consistent with emerging evidence in fenced lion populations, yet discordant with unfenced populations, which are usually ~2:1♂ in healthy, source populations. Our rarefied data suggest that a minimum of 4000 km search effort needs to be invested in future monitoring to obtain accurate and precise estimates, while assuming similar detection rates. This study demonstrates an important utility of Bayesian spatial explicit capture-recapture methods for obtaining robust estimates of lion densities and other important parameters in fence-protected areas to inform decision-making.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352093PMC
http://dx.doi.org/10.1002/ece3.10291DOI Listing

Publication Analysis

Top Keywords

bayesian spatial
12
long-term monitoring
8
african lions
8
spatial explicit
8
explicit capture-recapture
8
capture-recapture models
8
fenced protected
8
robust estimates
8
sex ratio
8
fenced
5

Similar Publications

The complex life cycle traits of amphibians make them especially sensitive to environmental change, and their ongoing conservation requires the maintenance of suitable habitat that accounts for such life cycle characteristics which may impacted by local environmental dynamics arising from climate change and human disturbance. Many existing studies on amphibian habitats disregard this important issue, leading to uncertainty in managing critical habitats. The application of appropriate conservation practices is therefore constrained by the fact that the major factors influencing amphibian habitats, and their spatio-temporal dynamics at different life stages, are poorly understood.

View Article and Find Full Text PDF

An examination of the effect of area-level characteristics on juvenile justice and child welfare referrals using multivariate Bayesian spatial modeling.

Child Abuse Negl

December 2024

The Ohio State University, College of Social Work, 300 Stillman Hall, 1947 North College Road, Columbus, OH 43210, United States of America. Electronic address:

Background: Neighborhood disadvantage is linked to a higher risk of referrals to child welfare and juvenile justice systems. While past research has explored these associations independently, no study has concurrently examined the spatial overlap of child maltreatment and juvenile justice involvement.

Objective: We examine the spatial overlap of involvement in juvenile justice and child welfare systems to identify areas of shared risk.

View Article and Find Full Text PDF

Investigating the potential of novel data mining algorithms (DMAs) for modeling groundwater quality in coastal areas is an important requirement for groundwater resource management, especially in the coastal region of Bangladesh where groundwater is highly contaminated. In this work, the applicability of DMA, including Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR) and Artificial Neural Network (ANN), for predicting groundwater quality in coastal areas was investigated. The optuna-based optimized hyperparameter is proposed to improve the accuracy of the models, including optuna-GPR and optuna-BRR as benchmark models.

View Article and Find Full Text PDF

Background: Adolescent girls and young women (AGYW) aged 15-24 years are more likely to acquire HIV than their male counterparts, and well-targeted prevention interventions are needed. We developed a method to quantify the risk of HIV acquisition based on individual risk factors and population viral load (PVL) to improve targeting of prevention interventions.

Setting: This study is based on household health survey data collected in 13 sub-Saharan African countries, 2015-2019.

View Article and Find Full Text PDF

Background: Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!