Leveraging AI to improve evidence synthesis in conservation

Oded Berger-Tal; Bob B.M. Wong; Carrie Ann Adams; Daniel T. Blumstein; Ulrika Candolin; Matthew J. Gibson; Alison L. Greggor; Malgorzata Lagisz; Biljana Macura; Catherine J. Price; Breanna J. Putman; Lysanne Snijders; Shinichi Nakagawa

doi:10.1016/j.tree.2024.04.007

Leveraging AI to improve evidence synthesis in conservation

Oded Berger-Tal, Bob B.M. Wong, Carrie Ann Adams, Daniel T. Blumstein, Ulrika Candolin, Matthew J. Gibson, Alison L. Greggor, Malgorzata Lagisz, Biljana Macura, Catherine J. Price, Breanna J. Putman, Lysanne Snijders, Shinichi Nakagawa

The Swiss Institute for Dryland Environmental and Energy Research

Ben-Gurion University of the Negev

Research output: Contribution to journal › Review article › peer-review

Abstract

Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and are used to support decisions and policies in a variety of applied fields, from medicine and public health to biodiversity conservation. However, conducting these exercises in conservation is often expensive and slow, which can impede their use and hamper progress in addressing the current biodiversity crisis. With the explosive growth of large language models (LLMs) and other forms of artificial intelligence (AI), we discuss here the promise and perils associated with their use. We conclude that, when judiciously used, AI has the potential to speed up and hopefully improve the process of evidence synthesis, which can be particularly useful for underfunded applied fields, such as conservation science.

Original language	American English
Pages (from-to)	548-557
Number of pages	10
Journal	Trends in Ecology and Evolution
Volume	39
Issue number	6
DOIs	https://doi.org/10.1016/j.tree.2024.04.007
State	Published - 1 Jun 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

artificial intelligence
biodiversity conservation
evidence synthesis
large language models
systematic reviews

All Science Journal Classification (ASJC) codes

Ecology, Evolution, Behavior and Systematics

Access to Document

10.1016/j.tree.2024.04.007

Cite this

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title = "Leveraging AI to improve evidence synthesis in conservation",

abstract = "Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and are used to support decisions and policies in a variety of applied fields, from medicine and public health to biodiversity conservation. However, conducting these exercises in conservation is often expensive and slow, which can impede their use and hamper progress in addressing the current biodiversity crisis. With the explosive growth of large language models (LLMs) and other forms of artificial intelligence (AI), we discuss here the promise and perils associated with their use. We conclude that, when judiciously used, AI has the potential to speed up and hopefully improve the process of evidence synthesis, which can be particularly useful for underfunded applied fields, such as conservation science.",

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doi = "10.1016/j.tree.2024.04.007",

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AU - Berger-Tal, Oded

AU - Wong, Bob B.M.

AU - Adams, Carrie Ann

AU - Blumstein, Daniel T.

AU - Candolin, Ulrika

AU - Gibson, Matthew J.

AU - Greggor, Alison L.

AU - Lagisz, Malgorzata

AU - Macura, Biljana

AU - Price, Catherine J.

AU - Putman, Breanna J.

AU - Snijders, Lysanne

AU - Nakagawa, Shinichi

PY - 2024/6/1

Y1 - 2024/6/1

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AB - Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and are used to support decisions and policies in a variety of applied fields, from medicine and public health to biodiversity conservation. However, conducting these exercises in conservation is often expensive and slow, which can impede their use and hamper progress in addressing the current biodiversity crisis. With the explosive growth of large language models (LLMs) and other forms of artificial intelligence (AI), we discuss here the promise and perils associated with their use. We conclude that, when judiciously used, AI has the potential to speed up and hopefully improve the process of evidence synthesis, which can be particularly useful for underfunded applied fields, such as conservation science.

KW - artificial intelligence

KW - biodiversity conservation

KW - evidence synthesis

KW - large language models

KW - systematic reviews

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U2 - 10.1016/j.tree.2024.04.007

DO - 10.1016/j.tree.2024.04.007

M3 - Review article

C2 - 38796352

SN - 0169-5347

VL - 39

SP - 548

EP - 557

JO - Trends in Ecology and Evolution

JF - Trends in Ecology and Evolution

IS - 6

ER -

Leveraging AI to improve evidence synthesis in conservation

Abstract

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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