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Spotting Cheetahs: Identifying Individuals by Their Footprints

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

Statistical matching for conservation science.

Judith Schleicher¹, Johanna Eklund², Megan D Barnes^3,4

¹Department of Geography, University of Cambridge, Cambridge, CB2 1QB, U.K.

Conservation Biology : the Journal of the Society for Conservation Biology

|November 30, 2019

Summary

This summary is machine-generated.

Statistical matching is key for conservation impact evaluations. Following best practices in planning, covariate selection, and quality checks ensures reliable assessment of conservation interventions.

Keywords:

autocorrelación espacial causal inference consecuencias indirectas conservation effectiveness counterfactual efectividad de la conservación evaluación de impacto hipótesis de contraste impact evaluation inferencia causal spatial autocorrelation spillover 保护有效性反事实因果推论效果评估溢出效应空间自相关

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Area of Science:

Conservation Science
Ecological Impact Assessment
Statistical Modeling

Background:

Growing need for rigorous impact evaluations in conservation science.
Increasing use of statistical matching techniques for assessing conservation interventions.
Potential pitfalls associated with matching approaches require careful consideration.

Purpose of the Study:

To outline important considerations and best practices for using matching in conservation science.
To guide researchers in conducting robust impact evaluations of conservation interventions.
To improve the quality and reliability of conservation impact assessments.

Main Methods:

Developing a clear theory of change to guide selection of treatment and control groups.
Selecting appropriate covariates and matching approaches based on study design.
Assessing the quality of matching through rigorous checks before outcome analysis.

Main Results:

Matching analysis involves three key steps: theory of change, covariate/approach selection, and quality assessment.
Steps two and three are iterative and must be completed before outcome exploration.
Improvements suggested include early evaluation planning, integrating qualitative methods, and considering spillover effects.

Conclusions:

Robust impact evaluations are crucial for effective conservation.
Adherence to best practices in statistical matching can enhance evaluation quality.
Further integration of planning, qualitative data, and spillover analysis is needed for future conservation impact studies.