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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Species distribution modeling (SDM) plays a crucial role in investigatinghabitat suitability and addressing various ecological issues. While likelihoodanalysis is commonly used to draw ecological conclusions, it has been observedthat its statistical performance is not robust when faced with slightdeviations due to misspecification in SDM. We propose a new robust estimationmethod based on a novel divergence for the Poisson point process model. Theproposed method is characterized by weighting the log-likelihood equation tomitigate the impact of heterogeneous observations in the presence-only data,which can result from model misspecification. We demonstrate that the proposedmethod improves the predictive performance of the maximum likelihood estimationin our simulation studies and in the analysis of vascular plant data in Japan.

Robust minimum divergence estimation in a spatial Poisson point process