English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Species distribution models are powerful tools on their own for the management of rare and at-risk species, but their utility as such can be limited if their accuracy is low, or if management policies are applied without consideration of the structure of the data. We tested the accuracy of a MaxEnt species distribution model for white-rimmed shingle lichen ( Fuscopannaria leucosticta ) in New Brunswick, Canada, and proposed an additional decision support-tool that matches the species probability of occurrence (PO) to possible management actions. The model was tested using two years of new field observations. Classification trees were used to define four groups across the range of PO values. Chi-squared tests were used to test the accuracy of predictions in each class. The model was found to accurately predict occurrences in very low and moderate classes, but underpredicted occurrences in low and high classes. We recommend using similar, data-driven support tools in the conservation decision-making process to minimize losses of the species and maximize efficacy of management.

Translating a species distribution model into management recommendations for an at-risk lichen