Biology subjects

Moeller, A. K.

Publications and source records attributed to Moeller, A. K..

3 recordsLinked to original sources

bioRxiv2026

Continent-wide calibration of camera-trap metrics reveals low population densities in the European wildcat

Effective conservation depends on demographic metrics that reliably reflect species status, particularly population abundance. For elusive species occurring at low densities, however, such metrics remain difficult to obtain. Spatial capture-recapture (SCR) models are the standardized approach for estimating density in marked populations, but their data requirements, especially the need for multiple spatial recaptures across individuals, often limit applicability in small or data-poor populations. This constraint has resulted in knowledge gaps for some of the most vulnerable species, undermining evidence-based conservation planning and management. Using camera-trap data and SCR-derived density estimates from data-rich populations, we evaluated alternative, less data-demanding metrics and tested the hypothesis: Space to Event (STE), Mean Local Abundance (MLA), and Relative Abundance Index (RAI) exhibit predictable relationships with SCR-derived density; if supported, these metrics can reliably estimate density in populations where SCR models cannot be implemented. We applied this framework to the European wildcat (Felis silvestris), an elusive small felid with highly fragmented populations across Europe, for which density estimates are largely lacking despite growing conservation concern. Across 21 study areas spanning most of the species range, our results indicate that European wildcats generally occur at lower densities than previously reported. SCR-derived estimates (n=10) averaged 10.32 {+/-} 11.56 inds/100km2, while STE enabled density estimation in five additional data-poor areas (mean 5.52 {+/-} 5.33 inds/100km2). STE showed a strong linear relationship with SCR-derived density (R2=0.98), supporting its use as a viable alternative when SCR is infeasible, although it tended to underestimate compared to SCR, especially at higher densities. In contrast, MLA and RAI showed weaker and non-linear relationships with SCR-derived density (R2=0.65), indicating substantially lower explanatory power and suggesting their estimates are more strongly influenced by confounding processes. By explicitly calibrating alternative metrics across a wide density gradient throughout most of the species distribution, this study provides a transferable methodological framework for estimating density in low-density wildlife populations and the first continent-wide, standardized density assessment of a carnivore species. From a management perspective, our findings identify populations that may be most vulnerable, particularly those with the lowest densities, and highlight the need to prioritize absolute abundance monitoring.

ecology↗

bioRxiv2025

Decreased female survival may explain wild turkey decline

Recent declines in wild turkey (Meleagris gallopavo) populations have prompted extensive research efforts and adjustments to state hunting regulations across the range of wild turkeys. However, research comparing historical and modern vital rates is needed to identify demographic factors that may explain declines. Based on matrix models, adult female survival has the greatest effect on the intrinsic rate of growth. Thus, we hypothesized that changes in annual female survival may explain contemporary declines in populations if that vital rate has decreased concomitant with population declines. We searched peer-reviewed literature, student theses and dissertations, and government reports for empirical estimates of annual female survival across the range of wild turkeys. Of the 51 resulting estimates of annual female survival across all subspecies, most were [≥]0.6 through 2012, but 8 out of 10 estimates post-2012 were lower, consistent with observed trends in population trajectories. We used matrix models to illustrate that the observed change in annual female survival estimates indeed predicts a declining population trajectory, even when other important vital rates are at the high end of the published range of process variation. If the published annual female survival estimates represent those experienced more broadly in populations, our meta-analysis and matrix models indicate that decreases in this vital rate help explain declines observed in populations in some regions. The causative factor(s) associated with the apparent declines are unknown, and little research has evaluated management strategies to increase annual female survival. Accordingly, we encourage wild turkey researchers to focus efforts on producing robust estimates of annual female survival, cause-specific mortality, and rigorous evaluation of management strategies to improve annual female survival.

zoology↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗