Estimation of a generalist meso-carnivore (black-backed jackal) population from a fenced protected area

Davidson-Phillips, Samuel Ralph

Title: Estimation of a generalist meso-carnivore (black-backed jackal) population from a fenced protected area
Creator: Davidson-Phillips, Samuel Ralph
Subject: Wildlife conservation
Subject: Carnivorous animals -- Conservation
Subject: Carnivorous animals -- Ecology
Date Issued: 2024-04
Date: 2024-04
Type: Master's theses
Type: text
Identifier: http://hdl.handle.net/10948/63698
Identifier: vital:73589
Description: Since 2017, landowners, field guides, and management staff have reported large groups of black-backed jackals (Lupullela mesomelas) (here-forward jackal) in the Welgevonden Game Reserve, Limpopo, South Africa. This is linked with several observations of jackals predating on various ungulate species, potentially leading to unintended consequences on prey populations. These observations combined with an apparent poor survival rate of impala (Aepyceros melampus) and continuous declines in their number led to the perception that jackals could be partly responsible. Several studies have attempted to describe the ecological role of jackals within multiple environments, most of which have proven to be variable and context dependent. Human-modified landscapes along with the fencing of protected areas, may have manipulated the role of jackal within these scenarios. Jackals are wide-ranging and generally not confined by fencing, therefore their population trends possibly fluctuate within these anthropogenic landscapes. Re-introduced apex predators have been shown to facilitate food (provision of carrion) and simultaneously suppress jackal (active killing), this, however, remains difficult to predict. Jackals are classified as facultative cooperative hunters, and the term describes how they hunt in groups opportunistically when suitable resources of prey are available. The indication by several studies that jackals do actively predate rather than only scavenge, illustrates that the species has the potential to cause declines in an ungulate population. It therefore appears erroneous to exclude the species in terms of predator-prey relationships, particularly for land managers of fenced protected areas. The first step to any ecological management is the understanding of population size and trends over time. Unfortunately, little to no reliable methods exist to assess or monitor jackal populations. A popular tool for cryptic and wide-ranging terrestrial carnivores is Spatial Capture Recapture (SCR) models, typically through a camera trap array. These often rely on individual identities and an imperfect detection process to derive a statistical estimate of a given area. Jackals have been assumed to be individually unidentifiable and therefore these methods have largely been excluded. To address this a pilot-targeted camera-trap survey was conducted to improve capture and image quality. Following the role of this procedure, semi-automated software was applied to test the feasibility of individual identifications of captured images. This resulted in a subset of 58 right and left identifiable flank images, compiled from the highest graded images (n = 220) using the open-source Interactive Individual IdentificationSystem Beta Contour 3.0 (I3S Contour). I3S Contour assists users by distinguishing between unique contours on independent flanks without omitting observer effort and ranking. The effectiveness of the identification procedure was evaluated using three software tool trials, namely Computer-aided Annotation, Manual Contour Annotation, and Manual Contour Annotation (MA-2), where MA-2 included additional user-defined meta-data to images. Results showed that jackals could be individually identified from camera trap images and thus opened up the use of previously excluded SCR methodologies. Utilising the jackal database derived from the identification procedures described a total of 28 complete identifications (both flanks matched), 32 left-sided and 36 right-sided captures were used. These were derived from two independent survey periods split between seasonality (Winter & Spring). Two SCR methods were compared, namely, the Spatially Explicit Camera Recapture (SECR) and the newly developed Spatial Presence-Absence (SPA) modelling approach. SECR relies on full individual identification linked to spatial locations to derive spatial parameters to estimate population densities. The SECR methodology has been considered the most precise and was thus used as the benchmark. SPA relies on detections only (i.e., without individual identities), along with informative or uninformative priors. This must be across a spatial array that has detectors close enough to allow for simultaneous detections during each occasion (< 24 hours). Comparisons between these model outputs indicated a high degree of confidence interval overlap; however, SPA had a consistently higher posterior mode density estimate (63-64% higher), where the coefficient of variation between outputs also indicated the SPA having a closer relative precision. The targeted survey results for both model outputs for 2021 did not appear unusually high when compared to other studies. To assess the WGR population size over the long term, opportunistic by-catch data from a nine-year leopard (Panthera pardus) camera survey (Panthera organisation) was utilised. Model outputs from each of the years indicated that population estimates remained relatively stable. This was an unexpected result as the SPA densities did not follow the detection observations. This could be attributed to M not being set high enough (200) and the model reaching the limit, resulting in similar outputs between years. An alternative explanation is where the station spacing is larger than the diameter of the home range, which may reduce spatial correlation.
Description: Thesis (MSc) -- Faculty of Science, School of Natural Resource Science & Management, 2024
Format: computer
Format: online resource
Format: application/pdf
Format: 1 online resource (xvii, 116 pages)
Format: pdf
Publisher: Nelson Mandela University
Publisher: Faculty of Science
Language: English
Rights: Nelson Mandela University
Rights: All Rights Reserved
Rights: Open Access

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