Dylan Childs

Dec 20, 2024 | mdpi.com | Dylan Childs

4. DiscussionThe rapid and accurate identification and quantification of arable weed populations over large areas are important tasks for modern agriculture. Several studies have now shown that the combination of remote sensing data and machine learning provides a promising route to achieve mapping of weed populations over the scales relevant for management. However, making accurate predictions at new sites from limited training data is a stumbling block identified by several studies [,,].

Oct 29, 2024 | onlinelibrary.wiley.com | Aldo Compagnoni |Dylan Childs |Roberto Salguero-Gómez

1 Introduction Understanding and predicting population dynamics is a key objective of Ecology (Sutherland et al. 2013). In population ecology, the last decades have witnessed the development and application of many statistical methods aimed precisely at this objective (Caswell 2001; Ellner, Childs, and Rees 2016).

Aug 4, 2024 | biorxiv.org | Aldo Compagnoni |Dylan Childs |Roberto Salguero-Gómez

AbstractUnderstanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool.