The crossroads of tradition and modern technology: integrative approaches to studying carnivores in low density ecosystems

The study of large carnivores in semi-arid ecosystems presents inherent challenges due to their low densities, extensive home ranges, and elusive nature. We explore the potential for the synthesis of traditional knowledge (i.e. art of tracking) and modern technology to address challenges in conservation and wildlife research in these challenging environments. Our research focuses on the African lion (Panthera leo) in the Central Kalahari region of Botswana as a model system to demonstrate the potential of this integrative approach. Combining GPS tracking and traditional San trackers’ expertise, we present two case studies: (1) the individual identification of lions via a combination of tracking and footprint analysis and (2) the monitoring of territorial behavior through a combination of GPS technology (i.e. GPS collars and handheld GPS devices) and non-invasive tracking. These approaches enhance our understanding of carnivore ecology as well as support conservation efforts by offering a non-invasive, cost-effective, and highly accurate means of monitoring populations. Our findings underscore the value of merging traditional tracking skills with contemporary analytical and technological developments to offer new insights into the ecology of carnivores in challenging environments. This approach not only improves data collection accuracy and efficiency but also fosters a deeper understanding of wildlife, ensuring the conservation and sustainable management of these species. Our work advocates for the inclusion of indigenous knowledge in conservation science, highlighting its relevance and applicability across various disciplines, thereby broadening the methodologies used to study wildlife, monitor populations, and inform conservation strategies

The challenge of monitoring elusive large carnivores: An accurate and cost-effective tool to identify and sex pumas (<i>Puma concolor</i>) from footprints

<div><p>Acquiring reliable data on large felid populations is crucial for effective conservation and management. However, large felids, typically solitary, elusive and nocturnal, are difficult to survey. Tagging and following individuals with VHF or GPS technology is the standard approach, but costs are high and these methodologies can compromise animal welfare. Such limitations can restrict the use of these techniques at population or landscape levels. In this paper we describe a robust technique to identify and sex individual pumas from footprints. We used a standardized image collection protocol to collect a reference database of 535 footprints from 35 captive pumas over 10 facilities; 19 females (300 footprints) and 16 males (235 footprints), ranging in age from 1–20 yrs. Images were processed in JMP data visualization software, generating one hundred and twenty three measurements from each footprint. Data were analyzed using a customized model based on a pairwise trail comparison using robust cross-validated discriminant analysis with a Ward’s clustering method. Classification accuracy was consistently > 90% for individuals, and for the correct classification of footprints within trails, and > 99% for sex classification. The technique has the potential to greatly augment the methods available for studying puma and other elusive felids, and is amenable to both citizen-science and opportunistic/local community data collection efforts, particularly as the data collection protocol is inexpensive and intuitive.</p></div

This figure shows the output when a relative likelihood slider is moved to the left, giving the relatively likelihood of 34 puma as 73.7%.

<p>This figure shows the output when a relative likelihood slider is moved to the left, giving the relatively likelihood of 34 puma as 73.7%.</p

The figure shows a model validation of the accuracy of classification in relation to the size of the test set against the training set.

<p> The varying test set size was plotted against itself (red), the predicted value for each test size iteration (black) and the mean predicted value for each test size (blue). Optimal classification accuracy was obtained when the test set size was smallest relative to the training set. However, the robustness of the model was demonstrated by the predicted value being close to the expected value, even when the test set was considerably larger than the training set (24:11).</p

This figure shows the classification dendrogram output from FIT.

<p> Three trails of 77 were misclassified (indicated by an x), giving an overall accuracy of individual identification for correct trail placement of 96.11%.</p

Footprint collection.

<p>Footprint collection.</p

Scatterplot showing the distribution of footprints by sex.

<p> Red stars are footprints from females, and blue from males. The black squares and circles are from animals of unknown sex from Big Bend (classified as female) and Camino Cielo (classified as male) respectively.</p

Pairwise comparisons.

<p>The figure shows the outcome of a pair-wise comparison of trails from the same individual (A) and two different individuals (B) based on a customized model in JMP software. The classifier incorporated into the model is based on the presence or absence of overlap between the ellipses. Note that the analysis is performed for each pairwise comparison in the presence of a third entity, i.e. the reference centroid value (RCV).</p

The number of variables extracted from the footprint images as lengths (L), angles and areas.

<p>The number of variables extracted from the footprint images as lengths (L), angles and areas.</p

A puma footprint showing the placement of 25 landmark points (red circles) and 15 points derived from them and generated by the FIT script (yellow circles).

<p>The landmark points and derived points are numbered in one sequence, providing 40 total points from which measurements (variables) of the footprint are made.</p