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A new method for identifying rapid decline dynamics in wild vertebrate populations.

ABSTRACT: Tracking trends in the abundance of wildlife populations is a sensitive method for assessing biodiversity change due to the short time-lag between human pressures and corresponding shifts in population trends. This study tests for proposed associations between different types of human pressures and wildlife population abundance decline-curves and introduces a method to distinguish decline trajectories from natural fluctuations in population time-series. First, we simulated typical mammalian population time-series under different human pressure types and intensities and identified significant distinctions in population dynamics. Based on the concavity of the smoothed population trend and the algebraic function which was the closest fit to the data, we determined those differences in decline dynamics that were consistently attributable to each pressure type. We examined the robustness of the attribution of pressure type to population decline dynamics under more realistic conditions by simulating populations under different levels of environmental stochasticity and time-series data quality. Finally, we applied our newly developed method to 124 wildlife population time-series and investigated how those threat types diagnosed by our method compare to the specific threatening processes reported for those populations. We show how wildlife population decline curves can be used to discern between broad categories of pressure or threat types, but do not work for detailed threat attributions. More usefully, we find that differences in population decline curves can reliably identify populations where pressure is increasing over time, even when data quality is poor, and propose this method as a cost-effective technique for prioritizing conservation actions between populations.

SUBMITTER: Fonzo MD

PROVIDER: S-EPMC3728972 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStudies of natural animal populations reveal widespread evidence for the diffusion of novel behaviour patterns, and for intra- and inter-population variation in behaviour. However, claims that these are manifestations of animal 'culture' remain controversial because alternative explanations to social learning remain difficult to refute. This inability to identify social learning in social settings has also contributed to the failure to test evolutionary hypotheses concerning the social learning strategies that animals deploy.Methodology/principal findingsWe present a solution to this problem, in the form of a new means of identifying social learning in animal populations. The method is based on the well-established premise of social learning research, that--when ecological and genetic differences are accounted for--social learning will generate greater homogeneity in behaviour between animals than expected in its absence. Our procedure compares the observed level of homogeneity to a sampling distribution generated utilizing randomization and other procedures, allowing claims of social learning to be evaluated according to consensual standards. We illustrate the method on data from groups of monkeys provided with novel two-option extractive foraging tasks, demonstrating that social learning can indeed be distinguished from unlearned processes and a social learning, and revealing that the monkeys only employed social learning for the more difficult tasks. The method is further validated against published datasets and through simulation, and exhibits higher statistical power than conventional inferential statistics.Conclusions/significanceThe method is potentially a significant technological development, which could prove of considerable value in assessing the validity of claims for culturally transmitted behaviour in animal groups. It will also be of value in enabling investigation of the social learning strategies deployed in captive and natural animal populations.

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A new method for identifying rapid decline dynamics in wild vertebrate populations.

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