Integrating time-lagged and cumulative landscape effects to understand contemporary spatial genetic structure of siberian roe deer (Capreolus pygargus)

Abstract Context Landscape genetic studies often rely on contemporary landscape snapshots, potentially underestimating the cumulative and time-lagged effects of historical landscapes on contemporary spatial genetic structure (SGS). Objectives We aim to assess isolation-by-resistance (IBR) of Siberian roe deer across 1995, 2000, 2005, 2010, 2015, and 2020 throughout the Lesser Xing’an Mountains, northeastern China, and identify landscape features driving gene flow and the effective time-lag interval. We also aim to quantify the cumulative impact of these six landscapes and to partition the genetic variation into cumulative IBR and isolation-by-distance (IBD). Methods We characterized the SGS of Siberian roe deer using 13 microsatellite loci and assessed single-year IBR with ResistanceGA. We then constructed an isolation-by-temporal-cumulative-resistance (IBtcR) model by weighting and integrating resistance distances across six temporal points, and partitioned genetic variation using redundancy analysis. Results IBR was influenced by topographical roughness, nighttime light, slope, and landscape type. Model selection identified a 25-year (1995–2020) time-lag interval, within which six historical landscapes contributed to SGS, with the 2005 landscape showing the strongest association (35.66%). Single-year IBR models had lower explanatory power than IBD but shared 59.7% of explained variance with IBD, whereas the IBtcR model outperformed all single-year IBR models and slightly outperformed IBD. Variance partitioning showed that IBtcR and IBD jointly explained 54.4% of SGS. Conclusions Historical landscapes over at least the past 25 years, together with IBD, have cumulatively shaped contemporary SGS. Our results refine interpretations of SGS derived from contemporary landscape snapshots, highlighting the importance of incorporating temporal perspectives into landscape-genetic frameworks.