Browsing by Subject "camera traps"
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Item Addressing challenges in camera-trap studies: Survey designs for multiple species, serial dependence, and site-to-site variability when estimating activity patterns(2020-07) Iannarilli, FabiolaCamera traps are widely used to collect information on the behavior, abundance, and occurrence of wild animals living in all parts of the world. Their low cost and ease of use makes it possible to collect information on many species simultaneously over large areas and long periods of time (Burton et al. 2015). These devices record the presence and activity of the animals that travel in front of their sensors. Most modern cameras rely on passive infrared sensors that are automatically triggered when a change in temperature is detected (Welbourne et al. 2016). They also have very low energy requirements and can be active for weeks without battery replacement, recording thousands of images along with the date and time each picture was taken. These characteristics allow users to continuously survey an area for long periods of time with minimal or no disturbance to wildlife, and make cameras extremely useful for surveying remote locations or studying animals that are particularly wary of human presence (e.g., carnivores). Decrease in cost per unit, technological advancements (e.g., switch from film to digital format, increase in trigger speed and storage capability), and development of statistical methods for estimating (relative) abundance and density have resulted in an exponential increase in the number of camera trap studies, especially those targeting secretive and elusive terrestrial vertebrates (Rovero and Zimmermann 2016). Additionally, the educational and outreach potential of the images collected, paired with the ease of use of camera devices, have led to the development of citizen science programs aimed at engaging the public in image processing and data collection (e.g., Snapshot Safari: snapshotsafari.org; eMammals: https://emammal.si.edu/). Camera traps were initially used for species checklists (Tobler et al. 2008) and for estimating population density for species in which individuals could be individually identified (Karanth and Nichols 1998), but they are now routinely used to quantify relative abundance (Rovero and Marshall 2009), occupancy (Pettorelli et al. 2010, Rich et al. 2016, Scotson et al. 2017), and density (Sun et al. 2017), to study animal behavior (Caravaggi et al. 2017) and diel activity patterns (Wang et al. 2015, Frey et al. 2017, 2020), and to investigate species richness and community composition at a global scale (Ahumada et al. 2011). Despite their widespread use, the field of camera trapping is still young and evolving, and several questions related to the use of this technology require further investigation (Meek et al. 2015). In this dissertation, I focus on two aspects related to survey design and analysis of camera-trap data: differential response across species to survey design strategies in studies aimed at simultaneously collecting data on multiple species (chapter 1), and statistical approaches to minimize or account for correlation when analyzing camera-trap data (chapter 2 and 3). In Chapter 1, I focus on differential responses of North-American carnivores to survey-design strategies often used in studies that target multiple species (i.e., multi-species studies). Cameras allow investigators to collect massive amounts of fine-resolution information on a large number of species simultaneously and have been described as an ideal, cost-effective tool for large-scale multi-species monitoring programs (Steenweg et al. 2017, Ahumada et al. 2019). Nevertheless, researchers have only recently started to quantify consequences of species-specific responses to survey-design strategies when the aim of the study goes beyond estimating species richness (Pease et al. 2016, Evans et al. 2019, Mills et al. 2019, Buyaskas et al. 2020, Holinda et al. 2020). Species’ characteristics, such as movement patterns, behavior and home range size affect their response to survey design; hence, in multi-species studies, a survey design that is appropriate for one species, might not work for another. This chapter was motivated by the Minnesota Department of Natural Resources’ (MN DNR) need for an approach capable of simultaneously monitoring multiple species of carnivores for informing conservation and management decisions. In collaboration with MN DNR biologists, I carried out a multi-year camera-trap study to assess species-specific responses to different survey strategies for 10 species of carnivores. I evaluated responses to two different survey-design frameworks (random- versus road-based), two different lure types (salmon versus fatty acid scent oil), two different placement strategies (completely random versus randomly-selected sites with feature-based placement), and survey timing (spring versus fall); I also assessed temporal trends in daily encounter probabilities within each season. I collected data at 100 locations in northern Minnesota during each of five 6-week long surveys between spring 2016 and spring 2018, accumulating more than 2 million pictures. Using Generalized Linear Mixed Models (GLMMs), I quantified species-specific responses to the different design strategies and found that differences were particularly strong for choice of survey-design framework and lure type. In Chapter 2, I illustrate how the lorelogram, a statistical tool for quantifying correlation in binary responses (Heagerty and Zeger 1998), can be used in ecological contexts to explore spatial and temporal correlation in binary data. Camera traps collect images near-continuously, resulting in data that are temporally correlated (Meek et al. 2014). A common strategy to minimize dependence among observations of a species collected at a certain site is data aggregation; images of a species are assumed to belong to the same encounter event whenever they occur at the same site and within some temporal threshold (e.g., 30 min apart). The temporal interval between subsequent pictures collected at the same site is often chosen arbitrarily (Burton et al. 2015) because methods to explore correlation in binary data (e.g., camera-trap detection/non-detection) are rare in the ecological literature. In this chapter, I show how the lorelogram, a statistical method commonly applied in the biomedical literature, can be used to describe correlation in camera-trap data and help with selecting appropriate time intervals to minimize serial correlation. Although the initial motivation behind this section of my dissertation was to provide a statistical tool for robustly selecting temporal intervals to minimize serial dependency in camera-trap data, the lorelogram can also be applied to other binary data exhibiting spatial or temporal correlation (e.g., distributional data). I demonstrate how the lorelogram can quantify spatial/temporal correlation using data from chapter 1 and data from the North-American Breeding Bird Survey (Pardieck et al. 2018). To facilitate the use of the lorelogram by analysts, I created an R-package that is freely available for download at https://github.com/FabiolaIannarilli/lorelogram. In Chapter 3, I illustrate how GLMMs can be used to describe diel activity patterns from camera-trap data. Understanding how individuals change their patterns of activity in response to the presence of competitors and natural and anthropogenic stressors is the goal of many ecological studies, and is important for predicting how species will adapt to future changes (e.g., climate change and land-use modifications). Camera-trap data collected at multiple sites are likely to be affected by site-to-site variability due to animals’ responses to differences in the biotic (e.g., presence of competitors; levels of human disturbance) and abiotic (e.g., proximity to roads; sampling treatments) characteristics of the sites sampled. Minimizing serial correlation by aggregating the data, as described in chapter 2, does not remove dependencies from repeated measures or account for site-to-site variability in frequency of site-use or in the timing of peak activity levels. Kernel Density Estimators (KDEs) are the most used approach to describe diel activity patterns from camera-trap data; circular KDEs, in particular, can be used to account for periodicities in diel activity patterns. This method, however, assumes independence among observations and, thus, ignores site-to-site variability common in camera-trap studies. In contrast, the GLMM approaches presented in this chapter can account for several types of variability (e.g., in frequency of site-use and correlation among repeated measures) using covariates and random effects; they also provide measures of the level of activity at sites characterized by different conditions. Like KDEs, the GLMM approach can accommodate the periodic nature of activity-pattern data, and can test hypotheses about changes in diel activity patterns due to biotic and abiotic factors, but they do so in a more direct way than KDEs (e.g., using AIC or Likelihood Ratio Tests). Using a simulation study, I compare the accuracy of GLMMs and KDEs when estimating activity patterns from camera-trap data. Using data from chapter 1, I also provide examples of how GLMMs can be used to explore diel activity patterns under different conditions (e.g., sampling treatments, seasons, or presence of competitors). Throughout the rest of the dissertation, I will use the first-person plural voice, ‘we’, to reflect the collaborative nature of the work presented. The second chapter of this dissertation is published in Methods in Ecology and Evolution (Iannarilli et al. 2019) and the material in chapter 1 is currently under review for publication in Wildlife Biology.Item Enhancing mammal conservation in multi-functional landscapes using artificial intelligence, joint species distribution modeling and ecological experimentation(2022-12) Velez Gomez, JulianaPoaching and livestock production threaten wildlife and its habitat, requiring strategies to manage human-dominated landscapes to sustain conservation objectives. To better understand the spatiotemporal distribution of wildlife and its response to disturbance factors (i.e., poaching and cattle), I deployed camera traps (CTs) and automated acoustic recording units (ARUs) on cattle ranches in the Colombian Orinoquía region. Data collection resulted in the challenge of processing “Big Data,” comprising a total of 824,883 images and 3,491,528 audio files (25,584 hours of recordings). In Chapter 1, I evaluated artificial intelligence platforms built for processing CT data and developed an open-source GitBook that illustrates the use and evaluation of model performance of each of these platforms. In Chapter 2, I used CT data to detect wildlife and trained machine learning algorithms for detecting cattle and poaching activity from the ARU data. To quantify co-occurrence patterns of poachers, cattle, and wildlife, I analyzed these data using joint species distribution models, finding that co-occurrence patterns between disturbance and wild ungulates were dependent on the data-collection method (i.e., whether CTs or ARUs were used to detect disturbance). Lastly, in Chapter 3, I conducted a cattle exclusion experiment to evaluate the effectiveness of fencing for reducing forest use and habitat degradation by cattle and improving resource availability for wildlife. Collectively, these efforts will guide management in multi-functional landscapes by identifying spatial co-occurrence patterns between wildlife and disturbance factors and by scaling up evidence-based interventions to optimize the use of remaining habitat by wildlife.Item Megaherbivores and the Maintenance of Biodiversity(2023-01) Huebner, SarahDue to alarming rates of wildlife decline throughout the world, ecological monitoring programs have become a critical component in evidence-based conservation planning. Continuous systematic monitoring using standardized camera trap grids helps to detect trends in wildlife population dynamics; however, the amount of data generated can be difficult to process in a timely manner. To mitigate this issue, citizen science and cutting-edge machine learning technologies can be combined to accelerate data collection, analysis, and reporting. In this dissertation, I will describe the creation, goals, and outcomes thus far of the Snapshot Safari project, an international, long-term ecological monitoring network using ~2000 camera traps and a hybrid data classification pipeline. Next, I’ll demonstrate the general utility of Relative Abundance Indices (RAIs) from camera trap data collected at five South African protected areas of varying sizes, management types, and herbivore assemblages by comparing them to RAIs from aerial surveys that were conducted during the same period. Finally, I will discuss the ‘elephant problem’ in South Africa and draw on three decades of long-term transect data on woody plant species combined with aerial surveys to examine the effects elephants exert on their ecological communities and, consequently, biodiversity at multiple trophic levels when they recolonize an area after extirpation.Item R code and data supporting: Cattle exclusion increases encounters of wild herbivores in Neotropical forests(2024-05-30) Vélez, Juliana; McShea, William; Pukazhenthi, Budhan; Rodríguez, Juan D; Suárez, María F; Torres, José M; Barrera, César; Fieberg, John; julianavelezgomez@gmail.com; Vélez, Juliana; Fieberg LabThis repository contains R code and data supporting: Cattle exclusion increases encounters of wild herbivores in Neotropical forests. This study implements a BACI experimental sampling design to quantify the effect of cattle exclusion on encounter probability of the native community of browsers and fruit consumers, and percent ground cover in multifunctional landscapes of the Colombian Orinoquía. Wildlife-permeable fences were built along forest edges in four forest patches (i.e., blocks) containing control and fenced (treatment) sites. We installed 33 camera traps to obtain information about wildlife and cattle encounter probabilities, before and after the fences were constructed. We fit Bayesian generalized linear mixed effects models to quantify the effect of fences via the interaction between the time period (before and after the fences were built) and treatment (control or fenced sites).Item R code and data supporting: Implications of the scale of detection for inferring co-occurrence patterns from paired camera traps and acoustic recorders(2023-09-05) Vélez, Juliana; McShea, William; Pukazhenthi, Budhan; Stevenson, Pablo; Fieberg, John; julianavelezgomez@gmail.com; Vélez, Juliana; University of Minnesota Fieberg Lab; Smithsonian's National Zoo and Conservation Biology InstituteThe objective of this study was to investigate the association between two measures of disturbance (poaching and livestock) and wild ungulates using data collected with camera traps and autonomous acoustic recording units. We quantified these associations using joint species distribution models (JSDMs) fit to data from multifunctional landscapes of the Orinoquía region of Colombia. We also evaluated the effect of the detection scale of camera traps and acoustic recorders for inferring co-occurrence patterns between wildlife and disturbance factors.Item Snapshot Safari Educational Materials(2020-11-16) Palmer, Meredith S; Dewey, Jessica; Huebner, Sarah; palme516@umn.edu; Palmer, Meredith S; University of Minnesota Lion Research CenterSnapshot Safari (www.snapshotsafari.org) is a cross-continental network of biodiversity monitoring programs run by the University of Minnesota Lion Center (www.lioncenter.umn.edu/snapshot-safari). To address the urgent need for accurately assessing vulnerable wildlife populations, we deployed over two dozen camera trap surveys distributed in protected areas across Africa. We rely on the help of online volunteers ("citizen scientists") to help classify animals captured in our millions of camera trap images. The citizen science platform provides a novel opportunity for public engagement and science education, and we have created educational multimedia based on the Snapshot Safari citizen science experience to promote these learning opportunities. Here, we present activities and videos aimed at a middle school-level audience that use our camera trap images to teach ecological and conservation principles.