Effect of habitat area and isolation on fragmented animal populations

Results and Discussion

Patch area and isolation were surprisingly poor predictors of occupancy across species. All together, 38% of patches were occupied (n = 55,855 occupancy records). We ran 4 logistic regression models for each population network to determine how well patch area and isolation predicted occupancy: (i) area only, (ii) isolation only, (iii) area + isolation, and (iv) area × isolation (see Methods). The amount of deviance (i.e., variation) in occupancy explained by each model (pseudoR², or pR²) represents the ability of patch area and/or isolation to predict species occurrence patterns; this statistic is analogous to the R² of linear regression (9). The pR² values from each of the 1,015 population networks created distributions that were skewed toward 0, and few species were strongly influenced by patch area or isolation (Fig. 1A). The full area × isolation model accounted for a median of 25% of the deviance in occupancy, indicating that at least 75% of occupancy deviance was caused by other factors for most population networks. Area was a better predictor of occupancy than isolation: area alone accounted for a median of 13% of the deviance in occupancy versus 3% accounted for by isolation alone. These results raise 2 questions: first, why does area explain more deviance in occupancy than isolation, and second, why are both metrics such poor predictors of occupancy, given that habitat destruction is the dominant threat to species persistence?

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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).

The stronger effect of patch area can be explained by examining the type of isolation measure used in the analysis. Patch isolation is measured in many ways (10), and we used 3 categories to define the isolation measure used in each study: distance to nearest habitat patch of any size (NH); distance to nearest large patch, or “mainland” (NM); or distance to nearest occupied source patch (NS). NH and NM, which are landscape isolation measures, were used for 94% of the population networks, whereas NS (a demographic isolation measure) was used for only 6%. When we limited analyses to studies that used NS, the importance of isolation in predicting occupancy was equal to that of area (Fig. S1). Likewise, for the 82 population networks for which we could calculate both NS and NH, NS had a much stronger effect on occupancy (paired t test on slopes of logistic regression, P < 0.0001). Previous studies that found stronger effects of area on occupancy often used landscape isolation measures (7, 11) or “isolated/not isolated” categories for each patch (12). NS is more relevant to metapopulation dynamics because the proximity of source populations, not habitat patches per se, affects colonization probabilities.

Although demographic isolation was a better predictor of occupancy than landscape isolation, area and demographic isolation were still poor predictors: the full area × isolation model explained a median of only 24% of the deviance in occupancy across the subset of studies (Fig. 1B). NS may be superior to NND as an isolation measure, but a previous study (10) indicated that NS was inferior to more complex measures that take into account the areas and distances of all potential source patches, known as model S_i. Data were available to calculate S_i for 24 population networks (including the 2 metapopulations used in the previous study), and our larger analysis showed that S_i did not explain any more variance in occupancy than NS did (see SI Text). Thus, NS appears to be as good a measure of isolation as the more complex measures that have been developed.

The overall poor performance of area and isolation as predictors of species occupancy could be caused by limitations of the data, ecological factors, or a combination of both. Snapshot occupancy data contain less information than population density or turnover data and may fail to capture the demographic advantages of large, highly connected habitat patches. Indeed, population networks that were monitored over multiple years had stronger relationships with area than those monitored for only 1 year (mean pR² = 0.27 for multiyear records vs. 0.17 for single-year, F_1,952 = 44.5, P < 0.0001). However, the relationship with isolation did not improve with additional years of monitoring (mean pR² = 0.09 for both types of studies, F_1,699 = 0.02, P = 0.90). Although this limitation likely explains some of the poor model fit, detailed examinations have suggested that occupancy data captures much of the information provided by population density data (13, 14). We propose 4 additional explanations for the low predictive power of patch area and isolation: (i) patches studied were of an inappropriate scale, such that areas and distances were not matched to focal species' body sizes and dispersal abilities; (ii) particular taxonomic groups or species with certain life history traits were less sensitive than others; (iii) most of the species were “survivors,” able to tolerate disturbance and not threatened with extinction; or (iv) the habitat island paradigm is not adequate in fragmented terrestrial systems because of strong effects of the matrix surrounding patches. We consider each of these.

Scale.

Assuming that researchers chose patch sizes that were appropriate to the body size of the study species, we would expect population networks from multispecies studies to be less sensitive to patch area, on average, than population networks from single-species studies. Most studies in this metaanalysis recorded the occupancy of multiple species in a single landscape, with body size ranges of up to 5 orders of magnitude, thus reducing the likelihood that patch sizes could be scaled appropriately for all species studied. Contrary to expectation, there was no relationship between the number of species in a study and the sensitivity of those species to patch area (n = 86 studies, R² = 0.002, P = 0.68). In addition, mean patch area was roughly scaled to body size across population networks (r = 0.52, n = 971). The small effect of area on occupancy therefore does not appear to be caused by inappropriate scaling.

Likewise, isolation sensitivity was not strongly influenced by dispersal limitation. A dispersal limitation index was calculated as: where dist_max is the maximum distance between patches in the landscape and disp_max is the maximum recorded dispersal distance for the species (n = 189). A species with a negative limitation index should not be dispersal limited. For example, a limitation index of −2 indicates that dispersal ability is 2 orders of magnitude greater than the maximum distance between patches. Limitation indices ranged from −3.2 for the red-tailed hawk (Buteo jamaicensis) in Mexico (41) to 2.9 for the northern pocket gopher (Thomomys talpoides) in Washington (42), and nearly half (49%) of the species were not dispersal-limited according to this index. Dispersal records are notoriously poor and often rely on chance observations, so maximum dispersal distances are likely underestimated for many species. Thus, the number of dispersal-limited species may be fewer than indicated by this index. If a lack of dispersal limitation explains the low influence of isolation on occupancy, then sensitivity to isolation should increase as dispersal limitation increases. There was a positive relationship between isolation sensitivity and dispersal limitation across species, indicating that the poor performance of isolation as a predictor of occupancy may be caused due by inappropriate scaling. However, the relationship between isolation sensitivity and dispersal limitation was weak, particularly for mammals and invertebrates (Fig. 2). Use of mean dispersal distances and mean or minimum patch distances to create the dispersal limitation index produced similar results. Although the scale of patch area and isolation may have been inappropriate for some species, scale issues are insufficient to explain the weak overall effect of area and isolation on species occurrence patterns.

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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).

Species Traits.

Sensitivity to area and isolation may have been low if particularly sensitive taxonomic groups, or species with certain life history traits, were poorly represented in the database. We examined the effects of taxonomic group, diet, specialization, habit (terrestrial or arboreal), and fecundity on area and isolation sensitivity by using general linear models. None of these factors significantly affected isolation sensitivity, but area sensitivity was affected by taxonomic group, diet, and habit (Fig. 3). Birds and mammals were most sensitive to area whereas amphibians were least sensitive, carnivores (including insectivores) were more sensitive than omnivores, and arboreal species were more sensitive than terrestrial species. It is notable that specialists, which should be more restricted to habitat patches than generalists, did not have increased sensitivity to patch area or isolation, despite inclusion of several individual studies that showed higher area sensitivity of specialists (e.g., ref. 43). This finding contrasts the idea that the habitat island paradigm fits terrestrial systems best when species that use matrix habitats are excluded from analyses (15, 16). The groups with relatively high area sensitivity (birds, mammals, and carnivores) were those with relatively high metabolic requirements rather than those with habitat restrictions; arboreal species were the only area-sensitive group with obvious ties to habitat requirements. Difficulty in defining and delineating habitat may partially explain the low area sensitivity of amphibians, because different habitats are often used during different life stages. Thus, delineated patches may underestimate the habitat actually used. These traits explain some of the variation in area sensitivity across species, but they do not explain the overall low effect of area on occupancy: area-sensitive groups were not poorly represented in the database and in fact tended to be better represented (Fig. 3).

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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.

Species Are Survivors.

The landscapes in this metaanalysis had been fragmented for at least 30 years, so it is possible that area and isolation effects were weak because the most sensitive species had disappeared before surveys. Indeed, only 5% of the mammals, birds, and amphibians in the metaanalysis were classified as threatened by the International Union for Conservation of Nature (IUCN). Worldwide, 20% of species in these taxonomic groups are threatened (17). However, IUCN threat status was unrelated to area and isolation sensitivity (n = 641, R² = 0.001, P = 0.39), indicating that low pR² values were not caused by a lack of threatened species in the database. For example, the critically endangered mountain yellow-legged frog (Rana muscosa) and the endangered Amani sunbird (Anthreptes pallidigaster) were both insensitive to patch area (pR² = 0.01 and 0.0003, respectively). Conversely, the 2 most area-sensitive species (the wood thrush, Hylocichla mustelina, and the veery, Catharus fuscescens) are ranked as “least concern” by the IUCN but are experiencing declines throughout their large ranges (ref. 18 and http://bna.birds.cornell.edu/BNA). Species in the database are “survivors” in that they have persisted through initial habitat destruction, but many of their populations are far from secure. Doomed populations can take far >30 years to disappear after fragmentation (19). This extinction debt could also lessen area effects, but we found that the reverse was true: population networks in systems with a natural matrix, which have generally been fragmented for thousands of years, were actually the least sensitive to area.

Patches Are Not Islands.

Unlike true oceanic islands, terrestrial habitat islands are not surrounded by a uniformly hostile matrix. Animals venturing outside of patches may find sufficiently benign conditions to live and reproduce, rendering the notion of the patch less relevant. We found strong effects of the matrix type on area and isolation sensitivity (Fig. 4). These broad patterns are particularly striking because the hostility of a matrix can vary widely among species within a landscape (20), and we therefore did not expect to find consistent effects of matrix type across species. Area sensitivity was higher in landscapes with human-dominated matrix types than in areas with natural matrix types. In contrast, isolation sensitivity was highest for population networks in forest patches surrounded by clear cuts. In these forestry-dominated landscapes, isolation effects were actually stronger than area effects even when landscape isolation measures were included.

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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.

The patch/nonpatch dichotomy appears to be a gross oversimplification for many species in fragmented landscapes. Irrespective of matrix effects, habitat patches often vary substantially in quality (21, 22). Many studies in this metaanalysis reported a strong influence of habitat quality on occupancy patterns. For example, patch area and isolation had little effect on the occupancy of most amphibians, whereas local habitat factors had stronger effects (45, 46). If habitat quality varies among patches, patch area may be a poor proxy of population size and thus unable to predict extinction probabilities accurately (23).

Habitat heterogeneity not only affects occupancy probabilities, but it also increases the difficulty of defining and delineating habitat patches in terrestrial landscapes (24). For example, extremely small patches may be ignored, and omission of small patches could lead to underestimates of area effects if those patches are empty. Furthermore, if some “nonhabitat” matrix areas are used by the animals, used patches may be both larger and closer together than those identified a priori by the researchers, thus contributing to the poor predictive power of area and isolation we detected.

Because habitat loss and fragmentation frequently co-occur, patch area and isolation may combine to reflect the influence of total habitat amount on a species (25). Previous work indicates that the total amount of habitat in a landscape may be the main determinant of population size and viability (26), and the spatial configuration of habitat does not influence occupancy until the proportion of habitat falls below a threshold (27–29). We were unable to test this hypothesis quantitatively because the total amount of habitat and nonhabitat in each landscape was unknown for most studies. However, maps of study areas in our metaanalysis generally showed that delineated habitat patches represented a small proportion of each landscape's total area. It is therefore unlikely that the poor predictive power of patch metrics across studies was caused by large amounts of identified habitat in each landscape.

Our empirical analysis of hundreds of animal species occurring in fragmented terrestrial landscapes throughout the world shows that patch area and isolation are poor predictors of occupancy in many cases. This finding does not mean patch metrics should be ignored altogether. Most species were more likely to be found in larger and less isolated patches, and some species were highly sensitive to patch area and isolation. However, the characteristics of intervening areas between patches, and of patches themselves, should not be ignored. The potential value of matrix areas for conservation has long been recognized (30–32), and our synthesis shows that landscape context has a strong effect on occupancy patterns across many taxonomic groups and ecosystems worldwide. The burgeoning body of research addressing matrix attributes should greatly increase our ability to predict and enhance species' persistence probabilities in fragmented systems (33–36). Identification of key features that determine whether a species will occupy an area should also improve both occupancy modeling and habitat restoration efforts. Minimizing the impact of threats that accompany habitat destruction, such as the spread of exotic species, overexploitation, and degradation of habitat within patches may have larger conservation returns than focusing on the amount and configuration of remaining habitat (37, 38), at least for those populations that persist in the aftermath of habitat destruction.