2022
DOI: 10.1101/2022.05.22.492972
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Assessing risk for butterflies in the context of climate change, demographic uncertainty, and heterogenous data sources

Abstract: Ongoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well-studied groups, like butterflies, information relevant to species-specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a miniscule fraction of insect diversity, including a few well-studied butterflies. Here we bring together heterogenous sources of information for… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
3
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1
1

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(3 citation statements)
references
References 93 publications
0
3
0
Order By: Relevance
“…Ecologists are harnessing new and previously overlooked data sources to better understand populations in our changing world (e.g., Gotelli et al 2021, Ellwood et al 2022, Di Cecco et al 2023), and smoothing splines have proven a useful tool in inferring population and phenology trends from insect monitoring data (Hodgson et al 2011, Wepprich et al 2019, Stemkovski et al 2020). However, rare and at-risk species are frequently under-represented in comparative studies of insect trends, due in part to the limited and messy data available for such species and the data requirements of standard methods (Forister et al 2023). We share our recommendations in the hopes that they will facilitate analyses of previously unusable data, and better inform our understanding of insect population dynamics and phenology.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Ecologists are harnessing new and previously overlooked data sources to better understand populations in our changing world (e.g., Gotelli et al 2021, Ellwood et al 2022, Di Cecco et al 2023), and smoothing splines have proven a useful tool in inferring population and phenology trends from insect monitoring data (Hodgson et al 2011, Wepprich et al 2019, Stemkovski et al 2020). However, rare and at-risk species are frequently under-represented in comparative studies of insect trends, due in part to the limited and messy data available for such species and the data requirements of standard methods (Forister et al 2023). We share our recommendations in the hopes that they will facilitate analyses of previously unusable data, and better inform our understanding of insect population dynamics and phenology.…”
Section: Discussionmentioning
confidence: 99%
“…However, the flexibility of smoothing splines leaves them prone to overfitting when working with sparse data, as is common for some types of population monitoring data. The simple solution of dropping years or populations with limited data will bias inferences made from estimated trends (Didham et al 2020), particularly since populations with more limited data have been found to be disproportionately at-risk species (Forister et al 2023). Here we offer specific approaches – pre-processing steps, model structures, and post-hoc analysis steps – that perform well for sparse monitoring data.…”
mentioning
confidence: 99%
“…The Melissa blue butterfly, L. melissa (family Lycaenidae) is distributed across much of the American west in a population structure that is patchy with low gene flow among individual locations [43]. Regional monitoring indicates that L. melissa is among a group of at-risk species, and that the family Lycaenidae in general contains a high concentration of declining species [45]. Additionally, although not occurring in the west, the related Karner blue butterfly ( Lycaeides samuelis or Lycaeides melissa samuelis) is a federally listed endangered species [46].…”
Section: Methodsmentioning
confidence: 99%