Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds

• Published in: Ecological modelling Vol. 408; p. 108743
• Main Authors: Fern, Rachel R., Morrison, Michael L., Wang, Hsiao-Hsuan, Grant, William E., Campbell, Tyler A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.09.2019
• Subjects: algorithms; biocenosis; Biotic interactions; Birds; breeding; breeding season; cacti and succulents; Cactus wren; case studies; Competition; data collection; forests; geographical distribution; Nesting; nests; Predation; regression analysis; Species distribution model; Verdin; Competition; Nesting; Species distribution model; Verdin; Cactus wren; Birds; Predation; Biotic interactions
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2019.108743

•Due to similar nesting ecology, Verdin are frequently subject to nest usurping by Cactus Wren.•We compared performance of SDMs with environmental factors only to those with a biotic component.•We used MaxEnt, Boosted Regression Tree, and Random Forest to predict Verdin presence.•SDM performance was improved by the inclusion of Cactus wren distribution. Complex, biotic interactions are notably excluded from species distribution models (SDMs) as they are often difficult to quantify and accommodate in a traditional modeling framework, especially those with a temporal component. The territorial nature of breeding Cactus wren is well-documented and typically involves nest usurping (i.e., destruction) of conspecifics. Due to their similar nesting ecology, breeding Verdin are frequently the target of such behavior and are often forced to move or abandon nests. Using the Verdin/Cactus wren system as a case study, our goal was to evaluate the performance of SDMs that include only environmental predictors with SDMs that also include biotic relationships as predictors. East Foundation’s San Antonio Viejo Ranch in south Texas. We built SDMs (MaxEnt, Boosted Regression Tree [BRT], and Random Forest [RF]) to project Verdin distribution during the early (April through mid-May), peak (mid-May through mid-June), and late (mid-June through mid-July) breeding periods using occurrence data collected during the 2015 and 2016 breeding seasons. We ran parallel analyses using relevant environmental features alone as predictors and then environmental features with observed Cactus wren density. Random Forest (RF) produced the highest predictive performance SDMs for all three breeding periods (AUC = 0.81-0.99; TSS = 0.23-0.73). All models improved in predictive power (Δ AUC = 0.01-0.10) and model sensitivity (Δ TSS = 0.09-0.66) with the inclusion of Cactus wren density as a predictor of Verdin presence. Our results indicate that SDM performance is improved by the inclusion of biotic relationships as predictors. Incorporating biotic interactions, as well as their temporal trends, is essential in efforts to monitor or conserve bird species with similar nesting ecologies. Further, modeling algorithms that can accommodate complex, non-linear relationships (e.g., Random Forest) should be preferred in SDM development and application.