Map of the study area: 9 estuaries in New Zealand North Island.

<p>Map of the study area: 9 estuaries in New Zealand North Island.</p

Box plots for β-conn and between-sites Jaccard’s dissimilarity in each estuary: 1, Mangemangeroa; 2, Okura; 3, Parekura; 4, Puhoi; 5, Tamaki; 6, Waitemata; 7, Waiwera; 8, Whananaki; 9, Whangateau.

<p>Boxplots include median, 25 and 75 percentiles and maximum and minimum values. In brackets the standard deviation of the β-diversities across each estuary.</p

Importance of the explanatory variables in the Random Forest models for β-site and β-conn measures.

<p>Importance of the explanatory variables in the Random Forest models for β-site and β-conn measures.</p

Ranges of the sediment variables across sites.

<p>Percentages of coarse sediment, medium-grain sand and mud, organic content and chlorophyll <i>a</i>.</p

Counting on β-Diversity to Safeguard the Resilience of Estuaries

<div><p>Coastal ecosystems are often stressed by non-point source and cumulative effects that can lead to local-scale community homogenisation and a concomitant loss of large-scale ecological connectivity. Here we investigate the use of β-diversity as a measure of both community heterogeneity and ecological connectivity. To understand the consequences of different environmental scenarios on heterogeneity and connectivity, it is necessary to understand the scale at which different environmental factors affect β-diversity. We sampled macrofauna from intertidal sites in nine estuaries from New Zealand’s North Island that represented different degrees of stress derived from land-use. We used multiple regression models to identify relationships between β-diversity and local sediment variables, factors related to the estuarine and catchment hydrodynamics and morphology and land-based stressors. At local scales, we found higher β-diversity at sites with a relatively high total richness. At larger scales, β-diversity was positively related to γ-diversity, suggesting that a large regional species pool was linked with large-scale heterogeneity in these systems. Local environmental heterogeneity influenced β-diversity at both local and regional scales, although variables at the estuarine and catchment scales were both needed to explain large scale connectivity. The estuaries expected <i>a priori</i> to be the most stressed exhibited higher variance in community dissimilarity between sites and connectivity to the estuary species pool. This suggests that connectivity and heterogeneity metrics could be used to generate early warning signals of cumulative stress.</p></div

Summary of the Random Forest (RF) and General Additive Model (GAM) for the diversity measures.

<p>Percentage of variance explained by each model and the significant effects of the variables at local and estuarine/catchment scales. All the GAMs were significant at p<0.001. Factor shell in the GAM is included as a categorical variable: “shell-p”, presence of shell, and “shell-r”, rare shell content.</p>***<p>p<0.001,</p>**<p>p<0.01,</p>*<p>p<0.05 and.: p<0.01.</p><p>“ns”: non-significant effects. +/− indicates the direction of the effects. s(factor) indicates smooth effects. “: ”crossed effects interaction.</p

Predicted changes of β-diversity, representing heterogeneity and connectivity measures across scales, under different stress scenarios.

<p>Predicted changes of β-diversity, representing heterogeneity and connectivity measures across scales, under different stress scenarios.</p

Principal Components of the estuarine and catchment environment data (arrows; data from Table 1) at each estuary: 1, Mangemangeroa; 2, Okura; 3, Parekura; 4, Puhoi; 5, Tamaki; 6, Waitemata; 7, Waiwera; 8, Whananaki; 9, Whangateau.

<p>PC1∶41% and PC2∶22% of explained variance.</p

Summary of the Mantel test.

<p>Significant results for the correlation between matrices of β-diversity (β-conn and Jaccard’s dissimilarity) with spatial (geographic distance) and environmental (Euclidean distance) data. For Jaccard’s dissimilarity the Mantel’s comparisons were done for matrices between pair of sites within each estuary and also between estuaries.</p

Estuarine and catchment characteristics.

<p>Data obtained from the New Zealand Estuarine Environment Classification Database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065575#pone.0065575-Hume1" target="_blank">[35]</a>.</p><p>Estuarine water area at high tide and land catchment area in km². Shore length in km. The shore complexity index (SC) as the length of the perimeter of the estuary shoreline divided by the circumference of a circle that has the same area as the estuary (1 simple and <0.1 complex shoreline). The closure index (CI) as the width of the estuary mouth divided by the length of the perimeter of the estuary shoreline (0.4 wide to <0.01 narrow entrance). Annual river inflow as the ratio of river inflow to total estuary volume at high water. The mean annual discharge of river to estuary in cumecs. Mean catchment rainfall (mm/yrs) and mean annual runoff (mm/km²). Mean tide range in metres. Intertidal, as the percentage of intertidal area in the estuary at high water. Land-use variables based on the percentage of catchment covered by natural, pastoral and exotic vegetation and urban areas.</p