Abstract. Landscape fragmentation alters biotic and abiotic characteristics of landscapes, variously affecting the size and demographic structure of species' populations. Fragmentation is predicted to negatively impact habitat specialists because of perturbations to their habitat, whereas generalists should be less sensitive to fragmentation. Differences in life history among the lizards in this community should partly explain some of the variation in generalist species' responses to fragmentation. During five seasons (2009-2013), we captured eight species of lizards on 27 independent trapping grids located in unfragmented (N = 18) and fragmented (N = 9) grids in the Mescalero-Monahans Sandhills ecosystem in southeastern New Mexico. Using a two-way ANOVA, we tested for effects of fragmentation and year on capture rates for each species. To test for effects of fragmentation on demographic structure, we used contingency tables with expected frequencies computed from the demographic structure on unfragmented grids. Capture rates of the endemic habitat specialist Sceloporus arenicolus (dunes sagebrush lizard) decreased to zero in fragmented sites. The demographic structure of S. arenicolus and Holbrookia maculata (common lesser earless lizard) was severely disrupted at fragmented sites, with proportions of juveniles, adult males, or adult females being over-or underrepresented during sample months. Variable responses of five generalist species could be attributed to life history patterns, habitat affinity, and breeding phenology. This is the first empirical study we are aware of that describes and quantifies the demographic effects of fragmentation on populations of multiple lizard species in a replicated study. Our findings lend important insights into how habitat specialization and differences in life history influence the susceptibility of species to the impacts of fragmentation.

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ContextAdvancements in camera-trap technology have provided wildlife researchers with a new technique to better understand their study species. This improved method may be especially useful for many conservation-reliant snake species that can be difficult to detect because of rarity and life histories with secretive behaviours. AimsHere, we report the results of a 6-month camera-trapping study using time lapse-triggered camera traps to detect snakes, in particular the federally listed Louisiana pinesnake (Pituophis ruthveni) in eastern Texas upland forests in the USA. MethodsSo as to evaluate the efficacy of this method of snake detection, we compared camera-trap data with traditional box-trapping data collected over the same time period across a similar habitat type, and with the same goal of detecting P. ruthveni. Key resultsNo differences in focal snake species richness were detected across the trap methods, although the snake-detection rate was nearly three times higher with camera traps than with the box traps. Detection rates of individual snake species varied with the trapping method for all but two species, but temporal trends in detection rates were similar across the trap methods for all but two species. Neither trap method detected P. ruthveni in the present study, but the species has been detected with both trap methods at other sites. ConclusionsThe higher snake-detection rate of the camera-trap method suggests that pairing this method with traditional box traps could increase the detection of P. ruthveni where it occurs. For future monitoring and research on P. ruthveni, and other similarly rare and secretive species of conservation concern, we believe these methods could be used interchangeably by saturating potentially occupied habitats with camera traps initially and then replacing cameras with box traps when the target species is detected. ImplicationsThere are financial and logistical limits to monitoring and researching rare and secretive species with box traps, and those limits are far less restrictive with camera traps. The ability to use camera-trap technologies interchangeably with box-trap methods to collect similar data more efficiently and effectively will have a significant impact on snake conservation.

Accurate identification of animal species is necessary to understand biodiversity richness, monitor endangered species, and study the impact of climate change on species distribution within a specific region. Camera traps represent a passive monitoring technique that generates millions of ecological images. The vast numbers of images drive automated ecological analysis as essential, given that manual assessment of large datasets is laborious, time-consuming, and expensive. Deep learning networks have been advanced in the last few years to solve object and species identification tasks in the computer vision domain, providing state-of-the-art results. In our work, we trained and tested machine learning models to classify three animal groups (snakes, lizards, and toads) from camera trap images. We experimented with two pretrained models, VGG16 and ResNet50, and a self-trained convolutional neural network (CNN-1) with varying CNN layers and augmentation parameters. For multiclassification, CNN-1 achieved 72% accuracy, whereas VGG16 reached 87%, and ResNet50 attained 86% accuracy. These results demonstrate that the transfer learning approach outperforms the self-trained model performance. The models showed promising results in identifying species, especially those with challenging body sizes and vegetation.

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