Underlying and proximate drivers of biodiversity changes in Mesoamerican biosphere reserves

Abstract

Protected areas are of paramount relevance to conserving wildlife and ecosystem contributions to people. Yet, their conservation success is increasingly threatened by human activities including habitat loss, climate change, pollution, and species overexploitation. Thus, understanding the underlying and proximate drivers of anthropogenic threats is urgently needed to improve protected areas' effectiveness, especially in the biodiversity-rich tropics. We addressed this issue by analyzing expert-provided data on long-term biodiversity change (last three decades) over 14 biosphere reserves from the Mesoamerican Biodiversity Hotspot. Using multivariate analyses and structural equation modeling, we tested the influence of major socioeconomic drivers (demographic, economic, and political factors), spatial indicators of human activities (agriculture expansion and road extension), and forest landscape modifications (forest loss and isolation) as drivers of biodiversity change. We uncovered a significant proliferation of disturbance-tolerant guilds and the loss or decline of disturbance-sensitive guilds within reserves causing a "winner and loser" species replacement over time. Guild change was directly related to forest spatial changes promoted by the expansion of agriculture and roads within reserves. High human population density and low nonfarming occupation were identified as the main underlying drivers of biodiversity change. Our findings suggest that to mitigate anthropogenic threats to biodiversity within biosphere reserves, fostering human population well-being via sustainable, nonfarming livelihood opportunities around reserves is imperative.

Keywords: anthropogenic disturbances; conservation success; deforestation; protected areas; species loss.

Fig. 1.

Hypothetical relationships between underlying drivers, proximate drivers, forest spatial changes, and their impact on biodiversity in protected areas. The underlying drivers result in different proximate forces, such as wood extraction, infrastructure extension, and agricultural expansion, which can directly determine forest spatial changes (33). Such spatial changes can affect both the composition (e.g., forest cover) and configuration (e.g., number of patches) of the landscape surrounding each protected area, ultimately shaping biodiversity trends over time.

Fig. 2.

Distribution of mean changes in abundance of all biodiversity guilds, disturbance-sensitive guilds, and disturbance-tolerant guilds during the last three decades in the studied Mexican biosphere reserves. The density plots resulted from bootstrap resampling with 10,000 iterations. The dashed line marks no change. In all cases, we found significant changes in abundance over time (estimated $\overline{\mathrm{x}}$ ≠ 0, P < 0.05).

Fig. 3.

Mean changes in abundance (±95% CI) of 31 biological guilds over the last 30 y in the studied Mexican biosphere reserves. The values derived from experts who provided information on relative changes of the biological guilds: 0, no change; 1, relative change <25%; 2, change ≥25% and <50%; and 3, changes ≥50% (see details in Materials and Methods). We used a bootstrap resampling with 10,000 iterations to estimate mean values and 95% CI. We considered that a change was significant if its 95% CI did not overlap zero (for details, see SI Appendix, S1 Appendix).

Fig. 4.

Structural equation models (SEM) of the relationships between underlying and proximate drivers of forest spatial changes and their effects on the diversity (PCA scores of mean richness and abundance) of disturbance-sensitive (A) and disturbance-tolerant guilds (B). Significant positive and negative paths are indicated with black and red arrows (P < 0.05, thin arrows, P < 0.01 thick arrows), respectively, whereas gray arrows indicate nonsignificant relationships (P > 0.05). Values near the arrows correspond to standardized coefficients and indicate the direction (positive/negative) and strength of each path. Note that an increase in interpatch isolation change indicates that isolation decreased through time. Therefore, the negative effect of this variable on the diversity of disturbance-tolerant guilds (B) implies that these guilds proliferated in biosphere reserves where interpatch isolation decreased. Within the box of each response variable, we also show the R² value. The fitting of the models with the data was consistently good (model a: Fisher’s C = 19.81, P = 0.70; model b: Fisher’s C=15.93, P = 0.89).