@article{f57de9867c4145c48d3734ebcadc37da,
title = "Evaluation options for wildlife management and strengthening of causal inference",
abstract = "Wildlife management aims to halt and then reverse the decline of threatened species, to sustainably harvest populations, and to control undesirable impacts of some species. We describe a unifying framework of three feasible options for evaluation of wildlife management, including conservation, and discuss their relative strengths of statistical and causal inference. The first option is trends in abundance, which can provide strong evidence a change has occurred (statistical inference) but does not identify the causes. The second option assesses population outcomes relative to management efforts, which provides strong evidence of cause and effect (causal inference) but not the trend. The third option combines the first and second options and therefore provides both statistical and causal inferences in an adaptive framework. We propose that wildlife management needs to explicitly use causal criteria and inference to complement adaptive management. We recommend incorporating these options into management plans.",
keywords = "causality, Biodiversity conservation, Adaptive management, strength of inference, population trends, adaptive management, biodiversity conservation",
author = "Jim Hone and Drake, {V. A.} and Charles Krebs",
note = "Funding Information: We thank the Institute for Applied Ecology, the University of Canberra, the Australasian Wildlife Management Society and the Southern African Wildlife Management Association for support. Sally Box, Peter Bridgewater, Nick Dexter, Sue Nichols, Astrida Upitis, Rudi van Aarde, and anonymous referees are thanked for useful discussions, comments and suggestions. This research did not receive any specific grant from funding agencies in the public, commercial, or notfor-profit sectors. JH received travel and conference funds from the Australasian Wildlife Management Society and the Southern African Wildlife Management Association, which are gratefully acknowledged. Publisher Copyright: {\textcopyright} The Author(s) 2023.",
year = "2023",
month = jan,
day = "12",
doi = "10.1093/biosci/biac105",
language = "English",
volume = "73",
pages = "48--58",
journal = "Bioscience",
issn = "0006-3568",
publisher = "American Institute of Biological Sciences",
number = "1",
}