Browsing by Subject "autonomous acoustic recording units"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Enhancing mammal conservation in multi-functional landscapes using artificial intelligence, joint species distribution modeling and ecological experimentation
    (2022-12) Velez Gomez, Juliana
    Poaching 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.
  • Thumbnail Image
    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 Institute
    The 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.