Browsing by Subject "forest elephant"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Open Access Can Movement Speed Predict Habitat Preference? Assessing the Influence of Topography, Village Proximity, and Land Cover on Forest Elephant (Loxodonta cyclotis) Movement and Preferences in Gabon.(2019-04-24) Kim, SeokminUnderstanding animal habitat preference is crucial for the management of animal populations and planning of protected areas. However, current models for estimating habitat preference require arbitrary estimates of habitat availability, which introduce a level of uncertainty and qualitative inference that could affect model accuracy. To overcome this problem, Dickson et al. (2005) suggested that movement speed could be used as a metric of habitat preference, assuming that movement speed would be negatively related to habitat preference. However, this speed - preference model ignores potential changes in movement related to behavioral shifts or variations in terrain. To assess the generalizability and practicality of the speed-preference model, I examined the hourly movements of 56 GPS collared forest elephants (Loxodonta cyclotis) in Gabon, central Africa between 2015 and 2018 in the context of three relevant environmental covariates (land covers, topography, and village proximity). I analyzed changes in movement speed by attributing a single value for specific environmental characteristics to each movement step and estimated preferences by calculating the density of each individual’s GPS points within the covariate of interest from the individual’s travel range. I then modeled the relationship between speed and preference with a linear mixed model. Speed failed to predict preferences for different land cover types, and relationships between speed and preference for gradients of topography and village proximity changed in both direction and intensity. Therefore, although using speed to predict habitat preference avoids the limitations of other habitat preference models, this method requires further research to assess the complex interactions between speed and environmental covariates for different animal species.Item Open Access Improving population estimates of difficult-to-observe species: A dung decay model for forest elephants with remotely sensed imagery(Animal Conservation, 2021-01-01) Meier, AC; Shirley, MH; Beirne, C; Breuer, T; Lewis, M; Masseloux, J; Jasperse-Sjolander, L; Todd, A; Poulsen, JRAccurate and ecologically relevant wildlife population estimates are critical for species management. One of the most common survey methods for forest mammals – line transects for animal sign with distance sampling – has assumptions regarding conversion factors that, if violated, can induce substantial bias in abundance estimates. Specifically, for sign (e.g. nests, dung) surveys, a single number representing total time for decay is used as a multiplier to convert estimated sign density into animal density. This multiplier is likely inaccurate if not derived from a study reflecting the spatiotemporal variation in decay times. Using dung decay observations from three protected areas in Gabon, and a previous study in Nouabalé-Ndoki National Park (Congo), we developed Weibull survival models to adaptively predict forest elephant (Loxodonta cyclotis) dung decay based on environmental variables from field collected and remotely sensed data. Seasonal decay models based on remotely sensed covariates explained 86% of the variation for the wet season and 79% for the dry season. These models included canopy cover, cloud cover, humidity, vegetation complexity and slope as factors influencing dung decay. With these models, we assessed sensitivity of elephant density estimates to spatiotemporal environmental heterogeneity, showing that our methods work best for large-scale studies >50 km2. We simulated decay studies with and without these variables in four Gabonese national parks and reanalyzed two previous surveys of elephants in Minkébé National Park, Gabon. Disregarding spatial and temporal variation in decay rate biased population estimates up to 1.6 and 6.9 times. Our reassessment of surveys in Minkébé National Park showed an expected loss of 78% of forest elephants over ten years, but the elephant abundance was 222% higher than previously estimated. Our models incorporate field or remotely sensed variables to provide an ecological context essential for accurate population estimates while reducing need for expensive decay field studies.