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Establishing large mammal population trends from heterogeneous count data.

R Pradel1,2, P-C Renaud2,3,4, O Pays5,6

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Wildlife managers can now model population trends from diverse census data using a new three-step method. This approach reconciles different count types and estimates growth rates, improving conservation efforts for various species.

Keywords:
Bayesian modelingheterogeneous wildlife censusespartial countspopbayes R packagepopulation rate of increasepopulation trendrelative growth ratetotal countswildlife management and conservation

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Area of Science:

  • Wildlife Biology
  • Conservation Science
  • Statistical Ecology

Background:

  • Wildlife population monitoring is crucial for conservation but challenged by heterogeneous time series data from various census methods (e.g., aerial, ground, expert estimates).
  • Existing methods struggle to integrate data from different sampling strategies, frequencies, and precision levels, hindering accurate trend analysis.

Purpose of the Study:

  • To present a novel three-step method for modeling wildlife population trends from heterogeneous time series data.
  • To develop a flexible and principled approach for analyzing diverse historical census data, enhancing conservation and management strategies.

Main Methods:

  • A heuristic for constructing credible intervals for all animal count data, including those lacking precision measures.
  • Development of conversion factors to standardize aerial and ground counts, with estimates provided for broad animal classes.
  • Implementation of a Bayesian model to estimate population growth rates and trends from reconciled counts, incorporating species-specific demographic potential.

Main Results:

  • The method successfully generates credible intervals for various count types and provides standardized conversion factors for different species classes.
  • The Bayesian model accurately estimates population trends and annual rates of increase, accounting for data precision and demographic limits.
  • A bespoke R package, popbayes, is developed for accessible implementation of the entire methodology.

Conclusions:

  • The presented three-step method offers a robust solution for analyzing heterogeneous wildlife population time series data.
  • The approach facilitates the principled use of all available historical census data, leading to more consistent and reliable trend estimates.
  • This flexible method is applicable to a wide range of animal species and can be adapted to test hypotheses about population dynamics.