Spatial modelling and landscape-level approaches for visualizing intra-specific variation.
Spatial analytical methods have been used by biologists for decades, but with new modelling approaches and data availability their application is accelerating. While early approaches were purely spatial in nature, it is now possible to explore the underlying causes of spatial heterogeneity of biological variation using a wealth of environmental data, especially from satellite remote sensing. Recent methods can not only make inferences regarding spatial relationships and the causes of spatial heterogeneity, but also create predictive maps of patterns of biological variation under changing environmental conditions. Here, we review the methods involved in making continuous spatial predictions from biological variation using spatial and environmental predictor variables, provide examples of their use and critically evaluate the advantages and limitations. In the final section, we discuss some of the key challenges and opportunities for future work.
Thomassen
Henri A
HA
Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, Los Angeles, CA 90095, USA. hathomassen@ucla.edu
Cheviron
Zachary A
ZA
Freedman
Adam H
AH
Harrigan
Ryan J
RJ
Wayne
Robert K
RK
Smith
Thomas B
TB
D003663:Decision Trees; D004463:Ecology / Q000379:methods*; D004777:Environment; D014644:Genetic Variation; D023281:Genomics; D008954:Models, Biological; D012044:Regression Analysis; D058998:Remote Sensing Technology; D012984:Software