Disentangling the interactive effects of anthropogenic disturbances on biodiversity

Abstract

Anthropogenic activity threatens biodiversity through climate change, habitat fragmentation, and increasing frequency and scale of disturbance. Various theoretical studies have sought to shed light on how these factors could promote or hinder the coexistence of species. However, our understanding of the relative importance of, and interactions between, these factors remains limited. In this study, we employ a theoretical approach integrating three commonly cited coexistence mechanisms -- the competition-colonisation trade-off, the intermediate disturbance hypothesis, and spatial heterogeneity -- into a unified model. We implement a novel method to integrate habitat autocorrelation into a system of differential equations, to create a simple and flexible model that can be used to investigate coexistence of multiple species arranged in a competitive hierarchy under different disturbance and habitat structure scenarios. Using this model, we find that considering interactions between different mechanisms is crucial for explaining the coexistence of species. Biodiversity patterns alternative to the uni-peak curve predicted by the intermediate disturbance hypothesis (e.g., bimodal) emerge along disturbance gradients as habitat fragmentation increases. Furthermore, habitat loss outweighs habitat autocorrelation effects in highly disturbed scenarios, yet autocorrelation can shape species coexistence under low disturbance. These findings underscore the need to integrate spatial and temporal mechanisms in biodiversity management.

Figures (10)

• Figure 1: Diagram representation of the model. Diagrams represent the distribution of rich (grey) and poor (white) patches for a landscape with $H$ = 0.5 and with either a high ($A$ = 1) (A) or low ($A$ = 0) (B) spatial autocorrelation index. Red circles represent a coloniser species with global dispersal ($d$ = 1), while blue circles are a competitor species with short dispersal ($d$ = 0). In plot (C) we show how the coloniser species (red line) has a $d$ = 1 which corresponds to a $\lambda$ = 0.5 for any spatial autocorrelation index, while the competitor species (blue line; $d$ = 0) has a $\lambda \sim$ 1 in spatially autocorrelated landscapes, but a $\lambda \sim$ 0.68 in non-autocorrelated landscapes (value estimated for a strategy dispersing following a Poisson dispersal kernel centred at a distance of 1 square in a spatial lattice; see main text for details). The orange line is for a species with $d$ = 0.5. Plots (D) and (E) display qualitative results for $L_R$\ref{['eq:3']} when $M_R$ = 0, and $M_R$\ref{['eq:4']} when $L_R$ = 0, respectively. In both cases, we use $H$ = 0.5, and we compare the results with $A$ = 0 (solid line) and $A$ = 1 (dashed line) for three levels of $\mu$ (0.01, 0.05, 0.1).
• Figure 2: Coexistence patterns for two species. Effects of spatial autocorrelation index ($A$), and proportion of habitat loss ($H$) on coexistence between a competitor ($c_M$ = $d_M$ = 0.2) and a coloniser ($c_L$ = $d_L$ = 0.9) with a competitive hierarchy. The mortality rate due to disturbances ($\mu$) is indicated on top of each plot. White cells indicate that no species survived. Results were computed using \ref{['eq:6']} and \ref{['eq:7']}, and the analytical result of the stability analysis was consistent with the coexistence area.
• Figure 3: Influence of spatial autocorrelation on biodiversity. Magnitude of the effects of spatial autocorrelation ($A$) depending on the proportion of habitat loss ($H$) and disturbance rate ($\mu$; indicated by different types of line). For the spatial-autocorrelation-induced variance (Y-axis) we gathered the Shannon effective diversity index obtained for all levels of $A$ (from 0 to 1) and a single value of $H$, and computed the variance of the indexes for each level of $H$. When varying $A$ has no effect on biodiversity, the Y-axis is zero, but it increases in value with increasing variance. We ran the model for A) five, B) 10 and C) 50 species, as indicated on the top of each panel. In all cases, species were given a matching competitive and dispersal ability ($d_i$ = $c_i$), and these values were equally spaced between 0.1 and 1, forming a competitive hierarchy.
• Figure 4: Co-occurrence of species and effective diversity for different environmental conditions. Results for the analytical model with 10 species, assuming a competitive hierarchy, with the competing/colonising abilities for each species $i$ ($c_i$ = $d_i$) equally spaced between 0 and 1 (left Y-axis). The grey cells indicate the presence of a species over a gradient of habitat loss, while white cells indicate its absence. The right Y-axis represents the effective diversity, and is plotted in green. The X-axis is the proportion of habitat loss ($H$). The disturbance level ($\mu$) is indicated on the left, while the spatial autocorrelation ($A$) is indicated on top.
• Figure 5: Diversity-disturbance patterns for 10 species. Figures show the effective diversity (Y-axis) depending on the mortality rate due to disturbance level (X-axis). We show diversity patterns for three levels of spatial autocorrelation (indicated on top of each panel, A-C). In all cases we show the results of the model for scenarios with low ($H$ = 0.1; continuous line) and intermediate ($H$ = 0.4; dotted line) levels of habitat loss.