The challenges that spatial context present for synthesizing community ecology across scales

Abstract

Accurately characterizing spatial patterns on landscapes is necessary to understand the processes that generate biodiversity, a problem that has applications in ecological theory, conservation planning, ecosystem restoration, and ecosystem management. However, the measurement of biodiversity patterns and the ecological and evolutionary processes that underlie those patterns is highly dependent on the study unit size, boundary placement, and number of observations. These issues, together known as the modifiable areal unit problem, are well known in geography. These factors limit the degree to which results from different metacommunity and macro-ecological studies can be compared to draw new inferences, and yet these types of comparisons are widespread in community ecology. Using aquatic community datasets, we demonstrate that spatial context drives analytical results when landscapes are sub-divided. Next, we present a framework for using resampling and neighborhood smoothing to standardize datasets to allow for inferential comparisons. We then provide examples for how addressing these issues enhances our ability to understand the processes shaping ecological communities at landscape scales and allows for informative meta-analytical synthesis. We conclude by calling for greater recognition of issues derived from the modifiable areal unit problem in community ecology, discuss implications of the problem for interpreting the existing literature, and identify tools and approaches for future research.

Figure 1.

Simple conceptual example of how decisions on where to define boundaries can have large impacts on diversity calculations. Communities are the colored dots and similarly colored dots have similar communities (A). The area of high turnover may be obscured (B) or highlighted (C) depending on boundary placement. The area of rapid turnover is where elevation is also changing rapidly (D).

Figure 2.

Metacommunities are often drawn as networks of completely connected sites (A) however, it is more likely that strong dispersal paths only exist in a subset of potential paths because of barriers or dispersal limitation (B). In this case (B), each habitat patch has a local metacommunity of habitat patches directly exchanging individuals with a focal patch (C,D,E), and the larger region can be conceptualized as multiple local metacommunity networks which share nodes in common and overlap (F).

Figure 3.

Visual comparison of β-diversity estimates produced by different sampling regimes. Upper map (A) displays all of the sites and are color coded by community composition, i.e. dots more similar to one another in color are also more similar to one another in fish community composition. Patterns in β-diversity produced by different sampling methods shown in lower panels (B: 40km window MESS, C: Counties, D: Physiographic Province, E: Huc12 Watershed). Inset box in B and C highlights effect of county line in C on β-diversity estimates.

Figure 4.

Comparing the relative importance of two environmental gradients in determining community composition (black line) to relative importance estimated by each spatial grouping approach (colored points). Left plot is a modeling run of a scenario where the latitudinal change is a gradual (z = 2.0), whereas the right plot is a scenario where the latitudinal change occurs rapidly over a short distance (z= 8.0).

Figure 5.

Relationship between region size and diversity estimates. Top row (A.-C.) shows the mean relationship (solid gray line) and 95% confidence interval (dotted gray lines) between size of sampling window and biodiversity. Selected relationships for individual sites (black dotted lines) highlight the range of patterns. The bottom row (D. – F.) shows the corresponding histograms of slopes for individual sites. For example, the relationship between β-diversity (measured as mean dissimilarity) and sampling area size tended to be weakly positive (B.).

Figure 6.

Integration of continental scale datasets for combined analysis. Matrix shows the spatial extent of data from each taxonomic group and the estimated values of mean α-richness, β-diversity, and γ-richness around each sampling location (lighter greys are higher diversity areas). Plots at right show that there are relationships between diversity measures that occur across taxonomic groups, even when there being different relationships within the different groups.