Effect of habitat area and isolation on fragmented animal populations

Abstract

Habitat destruction has driven many once-contiguous animal populations into remnant patches of varying size and isolation. The underlying framework for the conservation of fragmented populations is founded on the principles of island biogeography, wherein the probability of species occurrence in habitat patches varies as a function of patch size and isolation. Despite decades of research, the general importance of patch area and isolation as predictors of species occupancy in fragmented terrestrial systems remains unknown because of a lack of quantitative synthesis. Here, we compile occupancy data from 1,015 bird, mammal, reptile, amphibian, and invertebrate population networks on 6 continents and show that patch area and isolation are surprisingly poor predictors of occupancy for most species. We examine factors such as improper scaling and biases in species representation as explanations and find that the type of land cover separating patches most strongly affects the sensitivity of species to patch area and isolation. Our results indicate that patch area and isolation are indeed important factors affecting the occupancy of many species, but properties of the intervening matrix should not be ignored. Improving matrix quality may lead to higher conservation returns than manipulating the size and configuration of remnant patches for many of the species that persist in the aftermath of habitat destruction.

Fig. 1.

Strength of patch area and isolation effects on fragmented animal populations. Area explained more deviance in occupancy than isolation when all measures of isolation were included (A), whereas effects were equal when analyses were restricted to studies that used distance to nearest source population (NS) as the isolation measure (B). Isolation was measured as the distance to nearest patch, mainland, or source population in A. Four logistic regression models were run for each population network (when possible), with the following predictors of occupancy: (i) patch area, (ii) isolation, (iii) area + isolation, or (iv) area × isolation. The pR² value (a goodness-of-fit measure analogous to R² of linear regression) was recorded for each model. Box plots show medians (horizontal lines), interquartile ranges (boxes), the extent of nonoutlier datapoints (whiskers), and outliers (points).

Fig. 2.

Relationship between the dispersal limitation of a species and its sensitivity to patch isolation. The ability of patch isolation to predict occupancy was weakly related to dispersal limitation for all species combined (F_1,192 = 4.04, R² = 0.02, P = 0.05). See Scale in Results and Discussion for calculation of dispersal limitation. Relationships were stronger for amphibians and birds (A) than for invertebrates and mammals (B) but were not significant for any individual taxonomic group (amphibians: n = 7, R² = 0.45, P = 0.1; birds: n = 31, R² = 0.06, P = 0.2; mammals: n = 142, R² = 0.02, P = 0.06; invertebrates: n = 13, R² < 0.001, P = 0.98). Patch isolation included all 3 measures (nearest patch, mainland, or source). When restricted to nearest source, the relationship was similarly weak (F_1,30 = 3.31, R² = 0.10, P = 0.08). Alternative analyses using the slope parameter as a measure of effect size rather than pR² showed no relationship between dispersal limitation and isolation sensitivity (F_1,175 = 1.82, R² = 0.01, P = 0.18 with all measures; F_1,30 = 1.25, R² = 0.04, P = 0.27 with nearest source only).

Fig. 3.

Influence of species traits on the strength of patch area effects. The ability of patch area to predict occupancy was affected by the taxonomic group, diet, and habit of the species (full model F_7,930 = 14.2, R² = 0.10, P < 0.0001; taxon F₄ = 12.5, P < 0.0001, diet F₂ = 5.2, P = 0.005, habit F₁ = 7.7, P = 0.007). Diets were grouped such that “carnivore” included insectivores and parasitoids and “herbivore” included frugivores, nectivores, granivores, and detritivores. Habit was grouped such that “terrestrial” included fossorial and semiaquatic species. Least-squared means and SE bars are shown.

Fig. 4.

Effect of predominant land cover in the matrix surrounding habitat patches on the sensitivity of species to patch area (●) and isolation (○). Sensitivity was measured as the proportion of deviance in occupancy accounted for by patch area or isolation in logistic regression analyses (pR²). Analyses were weighted such that each study (i.e., landscape) contributed equally to the results (n = 52 agricultural landscapes, 7 forestry clearcut, 5 urban, and 25 natural). Patch area was a better predictor of occupancy in landscapes made patchy by human activities (agriculture, forestry, urbanization) than by natural processes (F_{3, 950} = 5.2, P = 0.002). Patch isolation (all measures included) best predicted occupancy in forest patches surrounded by clear cuts (F_{3, 697} = 40.3, P < 0.0001). Results were the same when analyses were restricted to studies using a demographic isolation measure (F_{3, 77} = 11.7, P < 0.0001). Error bars show 95% confidence intervals.