Faecal standing crop with real time correction using scat detection dogs to estimate population density.

Published online
13 Sep 2024
Content type
Journal article
Journal title
Journal of Applied Ecology
DOI
10.1111/1365-2664.14658

Author(s)
Santos Morini, R. F. dos & Grotta-Neto, F. & Duarte, J. M. B.
Contact email(s)
rubia.morini@unesp.br

Publication language
English
Location
Brazil

Abstract

Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Faecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find faecal samples in the field and to estimate the probability of which faeces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten faecal samples of grey brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 × 1440 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental faecal samples to determine the FSC parameters that were subsequently used with naturally occurring faecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 22.5% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.51 individuals km-2 (SD = 2.21, Dry) and 5.37 individuals km-2 (SD = 3.71, Wet). Synthesis and applications: Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability and climate in faecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.

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