Interpreting insect declines: seven challenges and a way forward
File(s)
Accepted version
OA Location
Author(s)
Type
Journal Article
Abstract
Many insect species are under threat from the anthropogenic drivers of global change. There have been numerous well‐documented examples of insect population declines and extinctions in the scientific literature, but recent weaker studies making extreme claims of a global crisis have drawn widespread media coverage and brought unprecedented public attention. This spotlight might be a double‐edged sword if the veracity of alarmist insect decline statements do not stand up to close scrutiny.
We identify seven key challenges in drawing robust inference about insect population declines: establishment of the historical baseline, representativeness of site selection, robustness of time series trend estimation, mitigation of detection bias effects, and ability to account for potential artefacts of density dependence, phenological shifts and scale‐dependence in extrapolation from sample abundance to population‐level inference.
Insect population fluctuations are complex. Greater care is needed when evaluating evidence for population trends and in identifying drivers of those trends. We present guidelines for best‐practise approaches that avoid methodological errors, mitigate potential biases and produce more robust analyses of time series trends.
Despite many existing challenges and pitfalls, we present a forward‐looking prospectus for the future of insect population monitoring, highlighting opportunities for more creative exploitation of existing baseline data, technological advances in sampling and novel computational approaches. Entomologists cannot tackle these challenges alone, and it is only through collaboration with citizen scientists, other research scientists in many disciplines, and data analysts that the next generation of researchers will bridge the gap between little bugs and big data.
We identify seven key challenges in drawing robust inference about insect population declines: establishment of the historical baseline, representativeness of site selection, robustness of time series trend estimation, mitigation of detection bias effects, and ability to account for potential artefacts of density dependence, phenological shifts and scale‐dependence in extrapolation from sample abundance to population‐level inference.
Insect population fluctuations are complex. Greater care is needed when evaluating evidence for population trends and in identifying drivers of those trends. We present guidelines for best‐practise approaches that avoid methodological errors, mitigate potential biases and produce more robust analyses of time series trends.
Despite many existing challenges and pitfalls, we present a forward‐looking prospectus for the future of insect population monitoring, highlighting opportunities for more creative exploitation of existing baseline data, technological advances in sampling and novel computational approaches. Entomologists cannot tackle these challenges alone, and it is only through collaboration with citizen scientists, other research scientists in many disciplines, and data analysts that the next generation of researchers will bridge the gap between little bugs and big data.
Date Issued
2020-03-04
Date Acceptance
2020-02-20
Citation
Insect Conservation and Diversity, 2020, 13 (2), pp.103-114
ISSN
1752-458X
Publisher
Wiley
Start Page
103
End Page
114
Journal / Book Title
Insect Conservation and Diversity
Volume
13
Issue
2
Copyright Statement
© 2020 The Royal Entomological Society. This is the peer reviewed version of the following article, which has been published in final form athttps://onlinelibrary.wiley.com/doi/full/10.1111/icad.12408. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Identifier
https://onlinelibrary.wiley.com/doi/full/10.1111/icad.12408
Subjects
Science & Technology
Life Sciences & Biomedicine
Biodiversity Conservation
Entomology
Biodiversity & Conservation
Citizen science
detection bias
global insect decline
insect conservation
monitoring
phenological shift
population trend
sampling bias
shifting baseline
time series
PINE BEAUTY MOTH
POPULATION-DYNAMICS
PANOLIS-FLAMMEA
SYNCHRONY
ABUNDANCE
TRENDS
RESTORATION
DIVERSITY
RICHNESS
BRITAIN
0502 Environmental Science and Management
0602 Ecology
0608 Zoology
Publication Status
Published
Date Publish Online
2020-03-04