Time-for-space substitution in N-mixture models for estimating population trends: a simulation-based evaluation

Andrea Costa,Sebastiano Salvidio,Marco Basile,Johannes Penner

doi:10.1038/s41598-021-84010-5

Andrea Costa, Sebastiano Salvidio + Show 2 more

Open Access

https://doi.org/10.1038/s41598-021-84010-5

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Journal: Scientific Reports	Publication Date: Feb 25, 2021
Citations: 9	License type: open-access

Affiliation: University of Genoa, University of Freiburg

Abstract

N-mixture models usually rely on a meta-population design, in which repeated counts of individuals in multiple sampling locations are obtained over time. The time-for-space substitution (TSS) in N-mixture models allows to estimate population abundance and trend of a single population, without spatial replication. This application could be of great interest in ecological studies and conservation programs; however, its reliability has only been evaluated on a single case study. Here we perform a simulation-based evaluation of this particular application of N-mixture modelling. We generated count data, under 144 simulated scenarios, from a single population surveyed several times per year and subject to different dynamics. We compared simulated abundance and trend values with TSS estimates. TSS estimates are overall in good agreement with real abundance. Trend and abundance estimation is mainly affected by detection probability and population size. After evaluating the reliability of TSS, both against real world data, and simulations, we suggest that this particular application of N-mixture model could be reliable for monitoring abundance in single populations of rare or difficult to study species, in particular in cases of species with very narrow geographic ranges, or known only for few localities.

Highlights

N-mixture models usually rely on a meta-population design, in which repeated counts of individuals in multiple sampling locations are obtained over time
Aim of this study is to provide an evaluation of the reliability of time-for-space substitution (TSS) applied to N-mixture models, and in particular to assess the reliability of abundance and trend estimations over several scenarios, including different population dynamics, initial abundance, detection probability, survey effort or duration of the study, by simulating count data from a single population surveyed several times per year and subject to different dynamics, and comparing real abundance and trend values with TSS estimates
Simulation scenarios accounting for a lower initial abundance (N = 5) experienced the higher T-error while the lower T-error value was achieved in larger populations (Fig. 1)

Summary

Introduction

N-mixture models usually rely on a meta-population design, in which repeated counts of individuals in multiple sampling locations are obtained over time. When dealing with species with a very narrow geographic range and few known populations, a spatially replicated monitoring protocol may be impracticable, and only data for single populations in successive years may be available In these situations, the application of time-for-space substitution (TSS) in the N-mixture modelling framework may be a suitable option to monitor a population on a single site over several y ears[20,21]. Aim of this study is to provide an evaluation of the reliability of TSS applied to N-mixture models, and in particular to assess the reliability of abundance and trend estimations over several scenarios, including different population dynamics, initial abundance, detection probability, survey effort or duration of the study, by simulating count data from a single population surveyed several times per year and subject to different dynamics, and comparing real abundance and trend values with TSS estimates

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