ANOVA output from the GLM Model 4b (Gaussian distribution, identity link function) indicating the significance of NO₃⁻ flux during daylight on the abundance of deposit feeders (DF) among treatments (OM: Organic matter, CC: Calcium carbonate, Mix: OM+CC, and C: Control).

<p>The DF*Treatment interaction (p = 0.07) effect is showed as contrasts (summary of the full regression-based model in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081646#pone.0081646.s009" target="_blank">Table S6</a>).</p><p>ns: <i>p</i>>0.1,</p>+<p>0.05</p><p>*<i>p</i><0.05,</p><p>**<i>p</i><0.01,</p><p>***<i>p</i><0.001.</p><p>Rs df: residual degrees of freedom; Rs Dev: residual deviance.</p

ANOVA output from the GLM Model 1b from Table 1 (Gaussian distribution, identity link function) indicating the significance of ammonium uptake (NH₄⁺) on gross primary production (GPP) among treatments (OM: Organic matter, CC: Calcium carbonate, Mix: OM+CC, and C: Control).

<p>No significant interaction was found (NH₄⁺*Tr; F_1,3 = 2.18, p = 0.106). The Treatment effect (p<0.05) sizes are showed as contrasts (see summary of the full regression-based model in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081646#pone.0081646.s006" target="_blank">Table S3</a>).</p><p>ns: <i>p</i>>0.1,</p>+<p>0.05<<i>p</i><0.1,</p><p>*<i>p</i><0.05,</p><p>**<i>p</i><0.01,</p><p>***<i>p</i><0.001.</p><p>Rs df: residual degrees of freedom; Rs Dev: residual deviance.</p

ANOVA output from the GLM Model 2b (Gaussian distribution, identity link function) indicating the significance of dissolved reactive phosphorus (DRP) on dissolved inorganic nitrogen (DIN) among treatments (OM: Organic matter, CC: Calcium carbonate, Mix: OM+CC, and C: Control).

<p>The DRP*Treatment (p<0.01) interaction effects are showed as contrasts (see summary of the full regression-based model in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081646#pone.0081646.s007" target="_blank">Table S4</a>).</p><p>ns: <i>p</i>>0.1,</p>+<p>0.05<<i>p</i><0.1,</p><p>*<i>p</i><0.05,</p><p>**<i>p</i><0.01,</p><p>***<i>p</i><0.001.</p><p>Rs df: residual degrees of freedom; Rs Dev: residual deviance.</p

The most parsimonious Generalized Linear Models (GLMs) relating different environmental and macrofauna community variables with and without treatment as a factor (terms that were not significant were dropped from the final models).

<p>The Akaike's Information Criterion (AIC) and the proportional increase in explained deviance (pseudo-R²) were used to evaluate each regression-based model fit and parsimony.</p><p>NH₄⁺: ammonium uptake; NO₃⁻: nitrate uptake; DIN: dissolved inorganic nitrogen (Σ NH₄⁺+NO₃⁻); NO₃⁻_light: nitrate flux during daylight.</p><p>DRP: dissolved reactive phosphorus (HPO₄²⁻- P); DIC_light: dissolved inorganic carbon flux during daylight.</p><p>GPP: gross primary production; Chla: chlorophyll <i>a</i> concentration.</p><p>Abundance: total macrofauna abundance; DF: deposit feeders abundance.</p><p>pseudo-R² = (null deviance-residual deviance)/null deviance; (<i>sensu</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081646#pone.0081646-Gray1" target="_blank">[46]</a>).</p>§<p>There was no significant interaction between the response variable and treatment (NH₄⁺*Tr). We dropped the interaction term from the analysis and ran the model again. Final Model 1b: GPP∼NH₄⁺+Tr (AIC = 683.4, pseudo-R² = 0.593).</p

ANOVA output from the GLM Model 3b (Gaussian distribution, identity link function) indicating the significance of chlorophyll <i>a</i> content on the total macrofauna abundance and the abundance of deposit feeders (DF) among treatments (OM: Organic matter, CC: Calcium carbonate, Mix: OM+CC, and C: Control).

<p>The Abundance*Treatment (p<0.05) and DF*treatment (p<0.01) interaction effects are showed as contrasts (see summary of the full regression-based model in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081646#pone.0081646.s008" target="_blank">Table S5</a>).</p><p>ns: <i>p</i>>0.1,</p>+<p>0.05<<i>p</i><0.1,</p><p>*<i>p</i><0.05,</p><p>**<i>p</i><0.01,</p><p>***<i>p</i><0.001.</p><p>Rs df: residual degrees of freedom; Rs Dev: residual deviance.</p

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

Relationship between nitrate flux during daylight (µmol NO₃⁻_light m⁻²h⁻¹) and the abundance of deposit feeders.

<p>The panel shows the general relationship across the sampling area. Plots are raw data. (Treatments: Organic Matter, Calcium carbonate, Mix, Control).</p

Biplots showing the relationship between chlorophyll <i>a</i> vs. macrofauna abundance, and the main feeding guilds.

<p>The panels show the general relationship across the sampling area. Chlorophyll <i>a</i> (µg g⁻¹ sediment), total macrofauna abundance (number of individuals), main feeding guilds (abundance of deposit and suspension feeders; DF and SF, respectively). Plots are raw data. (Treatments: Organic Matter, Calcium carbonate, Mix, Control).</p

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

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