American Society of Naturalists

A membership society whose goal is to advance and to diffuse knowledge of organic evolution and other broad biological principles so as to enhance the conceptual unification of the biological sciences.

Synthesis: “Context dependence of local adaptation to abiotic and biotic environments: a quantitative and qualitative synthesis”

Posted on

Ryan D. Briscoe Runquist, Amanda J. Gorton, Jeremy B. Yoder, Nicholas J. Deacon, Jake J. Grossman, Shan Kothari, Marta P. Lyons, Seema N. Sheth, Peter Tiffin, and David A. Moeller (March 2020)

Metasynthesis reveals context dependent local adaptation to abiotic and biotic factors but bias in experimental design

Read the Article (Just Accepted)

Using a novel synthesis approach, researchers find new insights into local adaptation to abiotic and biotic factors and how scientists design experiments

Local adaptation occurs when environmental variation across the landscape causes changes in traits that optimize fitness in alternative environments. Decades of studies have shown that local adaptation is common and occurs in response to a wide variety of environmental factors. But what types of environmental variables lead to the greatest expression of local adaptation? Is it abiotic variables (e.g. temperature) or biotic variables (e.g. competition)? Furthermore, how is the importance of these variables influenced by the ways in which scientists design and conduct experiments to test local adaptation?

Briscoe Runquist and others at the University of Minnesota combined two summary approaches to address these questions. They first used a meta-analysis to summarize data from 31 papers that manipulated both abiotic and biotic factors simultaneously. They found that biotic factors have stronger effects on fitness than abiotic factors. The extent of local adaptation was context-dependent: populations expressed greater local adaptation to their abiotic environment when they also experienced their home biotic environment. They also found the fitness effects of biotic factors was greater at low than high latitudes whereas the opposite was true for abiotic factors, consistent with longstanding predictions.

The researchers then used a qualitative meta-synthesis to analyze the text of articles to identify common themes and experimental biases in studies of local adaptation. Meta-syntheses are common in medical and social sciences but are new to ecology and evolution. The researchers found that the selection of abiotic and biotic variables was often biased towards extreme implementations (presence/absence, rare environments) that do not reflect the gradients often found in nature. Further, tests of local adaptation were nearly always conducted on short-lived, sessile organisms even though these are relatively uncommon in nature. Last, they identified opportunities moving forward for planning experiments that incorporate greater natural variation in abiotic and biotic environments.


Understanding how spatially-variable selection shapes adaptation is an area of longstanding interest in evolutionary ecology. Recent meta-analyses have quantified the extent of local adaptation, but the relative importance of abiotic and biotic factors in driving population divergence remains poorly understood. To address this gap, we combined a quantitative meta-analysis and a qualitative meta-synthesis to (1) quantify the magnitude of local adaptation to abiotic and biotic factors and (2) characterize major themes that influence the motivation and design of experiments that seek to test for local adaptation. Using local-foreign contrasts as a metric of local adaptation (or maladaptation), we found that local adaptation was greater in the presence than absence of a biotic interactor, especially for plants. We also found that biotic environments had stronger effects on fitness than abiotic environments when ignoring whether those environments were local versus foreign. Finally, biotic effects were stronger at low latitudes and abiotic effects were stronger at high latitudes. Our qualitative analysis revealed that the lens through which local adaptation has been examined differs for abiotic and biotic factors. It also revealed biases in the design and implementation of experiments that make quantitative results challenging to interpret and provided directions for future research.