Importance of antecedent environmental conditions in modeling species distributions. Issue 5 (3rd October 2017)

• Record Type: Journal Article
• Authors: Ryo, Masahiro; Yoshimura, Chihiro; Iwasaki, Yuichi
• Is Part Of: Ecography. Volume 41:Issue 5(2018)
• Page Start: 825
• Page End: 836
• Publication Date: 2017-10-03
• Subjects: Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.02925
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Although species distributions can change in an unexpectedly short period of time, most species distribution models (SDMs) use only long‐term averaged environmental conditions to explain species distributions. We aimed to demonstrate the importance of incorporating antecedent environmental conditions into SDMs in comparison to long‐term averaged environmental conditions. We modeled the presence/absence of 18 fish species captured across 108 sampling events along a 50‐km length of the Sagami River in Japan throughout the 1990s (one to four times per site at 45 sites). We constructed and compared the two types of SDMs: 1) a conventional model that uses only long‐term averaged (10‐yr) environmental conditions; and 2) a proposed model that incorporates environmental conditions 2 yr prior to a sampling event (antecedent conditions) together with long‐term averages linked to life‐history stages. These models both included geomorphological, hydrological, and sampling conditions as predictors. A random forest algorithm was applied for modeling and quantifying the relative importance of the predictors. For seven species, antecedent hydrological conditions were more important than the long‐term averaged hydrological conditions. Furthermore, the distributions of two species with low prevalence could not be predicted using long‐term averaged hydrological conditions but only using antecedent hydrological conditions. In conclusion, incorporating antecedent environmental factors linked with life‐history stages at appropriate time scales can better explain changes in species distribution through time.