Habitat fragmentation increases specialization of multi-trophic interactions by high species turnover

Abstract

Habitat fragmentation is altering species interactions worldwide. However, the mechanisms underlying the response of network specialization to habitat fragmentation remain unknown, especially for multi-trophic interactions. We here collected a large dataset consisting of 2670 observations of tri-trophic interactions among plants, sap-sucking aphids and honeydew-collecting ants on 18 forested islands in the Thousand Island Lake, China. For each island, we constructed an antagonistic plant-aphid and a mutualistic aphid-ant network, and tested how network specialization varied with island area and isolation. We found that both networks exhibited higher specialization on smaller islands, while only aphid-ant networks had increased specialization on more isolated islands. Variations in network specialization among islands was primarily driven by species turnover, which was interlinked across trophic levels as fragmentation increased the specialization of both antagonistic and mutualistic networks through bottom-up effects via plant and aphid communities. These findings reveal that species on small and isolated islands display higher specialization mainly due to effects of fragmentation on species turnover, with behavioural changes causing interaction rewiring playing only a minor role. Our study highlights the significance of adopting a multi-trophic perspective when exploring patterns and processes in structuring ecological networks in fragmented landscapes.

Keywords: antagonistic network; bottom-up effect; interaction rewiring; mutualistic network; plant–aphid–ant interaction.

Figure 1.

(a) The 18 study islands (in black) in the Thousand Island Lake, Zhejiang, China. (b–g) Exemplary trophobioses illustrating the diversity of participating plants, aphids and ants in this study. (b) Pheidole sp1 ant tending the aphid Aulacophoroides hoffmanni on the plant Wisteria sinensis. (c) Polyrhachis dives tending Eutrichosiphum pasaniae on Castanopsis sclerophylla. (d) Pheidole nodus tending Aphis sp1 on Gardenia jasminoides. (e) P. dives tending Aphis sp1 on Eurya muricata. (f) Polyrhachis dives tending Aphis eugeniae on Glochidion puberum. (g) Pheidole nodus tending Greenidea kuwanai on Quercus acutissima. Photos (b–g) taken by Xue Zhang.

Figure 2.

Changes in network specialization (H₂′) of plant–aphid (orange) and aphid–ant (blue) networks along area (a) and isolation (b) gradients on 18 islands of the Thousand Island Lake, China. The linear fitted lines are based on multiple linear regression models (see §2, Material and methods). Island area (ha) was log-transformed to normalize model residuals. Solid lines indicate significant relationships (p < 0.05), whereas dotted lines represent non-significant relationships (p > 0.05). Shaded polygons specify the 95% confidence interval.

Figure 3.

Comparison of components of plant–aphid and aphid–ant network dissimilarities among islands: total network dissimilarity (β_WN), turnover in species composition (β_ST) and interaction rewiring (β_OS) among 18 islands of the Thousand Island Lake, China. Boxes indicate the first and third quartiles (Q1 and Q3), horizontal lines inside boxes are medians, vertical lines indicate Q1/Q3 + 1.5 × interquartile ranges (IQR) and points are outliers. Asterisks indicate significant differences (p < 0.001) between β_ST and β_OS via a two-tailed t-test for both plant–aphid and aphid–ant networks.

Figure 4.

Direct and indirect links of habitat fragmentation (island area, isolation), species pairwise abundance-weighted β diversity (plant dissimilarity, aphid dissimilarity, ant dissimilarity) and difference in network specialization for plant–aphid and aphid–ant networks on 18 islands of the Thousand Island Lake, China. (a) ‘Bottom-up’ model, i.e. when plant dissimilarity is hypothesized to predict aphid dissimilarity, and aphid dissimilarity is hypothesized to predict ant dissimilarity. (b) The paths for the possible opposite scenario, i.e. a ‘top-down’ effect where ant dissimilarity is hypothesized to predict aphid dissimilarity and aphid dissimilarity is hypothesized to predict plant dissimilarity. All arrows in the figure represent positive relationships. Numbers under each response variable indicate the R² for each individual MRM (multiple regression for matrix). Numbers alongside arrows indicate the range-standardized effect size of each predictor variable. Asterisks denote significance levels: ***p < 0.001, **p < 0.01, *p < 0.05. AICs of the two path models are: AIC = 56.56 for bottom-up model; AIC = 56.07 for top-down model.

Figure 5.

Changes in relative specialization (rH₂′) along island area (a) and isolation (b) gradients of plant–aphid and aphid–ant networks on 18 islands of the Thousand Island Lake, China. The linear fits are based on multiple linear regression models. For each island, we derived an expected interaction network with the same species from the regional network (i.e. a landscape-level network that consists of information gathered across 18 island networks). Interaction frequency of each pairwise interaction was inherited from the regional network. Relative specialization for each island was calculated based on specialization in the observed network minus the specialization in the expected network. Thus, relative specialization was used to estimate the difference in specialization due to interaction rewiring. Plant–aphid networks are represented by orange and aphid–ant networks are represented by blue. Island area (ha) was log-transformed to normalize model residuals. Solid lines indicate significant relationships (p < 0.05), whereas dotted lines represent non-significant relationships (p > 0.05). Shaded polygons specify the 95% confidence interval.