Predicting the response of nekton assemblages to seagrass transplantations in the Venice lagoon: An approach to assess ecological restoration

Abstract

1. One of the major challenges to ensure effective restoration of estuarine habitats is to establish success criteria to determine whether the goals of restoration are met.2. The aim of this work is to propose and test an approach to identify reference conditions and assess the recovery of nekton (fish, decapods and cephalopods) assemblages at seagrass restoration sites.3. Nekton sampling took place from 2014 to 2017 in the northern Venice lagoon (northern Adriatic Sea, Italy) during spring at eight sites subjected to seagrass (Zostera marina and Zostera noltei) transplantation. In spring 2016, five natural seagrass sites in the same area were additionally sampled, and physico-chemical water parameters and habitat structure were also recorded. 4.A multivariate generalized linear model approach was adopted in order to disentangle the relative effect of water quality and seagrass habitat structure on nekton assemblages of natural habitats. Models were subsequently employed to predict species composition of nekton fauna at each transplantation site, to identify the reference assemblage expected under site-specific abiotic and habitat characteristics. 5. The average distance of the observed assemblage from reference conditions was used to track temporal trajectories of nekton colonization at transplantation sites, and to ultimately evaluate the recovery rate towards restoration goals.6. This study highlights how a predictive approach could serve management purposes in ecological restoration, providing a concise tool to assess the functionality of restored habitats for associated fauna.

Expected Shifts in Nekton Community Following Salinity Reduction: Insights into Restoration and Management of Transitional Water Habitats

“…A value of sediment grain size (percentage of sand in the 10 cm surface layer) was finally associated to each sampling site using data from previous studies [71][72][73]. Field data were employed in a model framework to identify factors driving nekton variability and then predict assemblage characteristics under scenarios of salinity reduction, following the approach proposed for the restoration of seagrass meadows in the Venice lagoon by Scapin et al [36]. Negative binomial generalised linear models (GLMs) were fitted to biomass of each species contributing to 98% of total assemblage biomass.…”

“…Five model categories were included, investigating the following hypotheses ( Table 1): None of the predictors considered affect the response variable (null model: category m0); the response variable is affected by the temporal factor only (seasonal and inter-annual variability; category m1); the response variable responds to both temporal factors and the sub-basin (category m2); the response variable is affected by temporal factors, the sub-basin and environmental characteristics of water and bottom surface (category m3); the response variable responds to temporal factors, the sub-basin, environmental characteristics and location (the latter specified either as saltmarsh creek or edge) (category m4). The relative influence of each predictor on variability of nekton assemblage was hence evaluated by comparing the different model formulations (Table 1) in a hierarchical framework [36,41]. Following the approach of the manyglm function contained in the mvabund software package [74], the inference was carried out at the assemblage Field data were employed in a model framework to identify factors driving nekton variability and then predict assemblage characteristics under scenarios of salinity reduction, following the approach proposed for the restoration of seagrass meadows in the Venice lagoon by Scapin et al [36].…”

“…The relative influence of each predictor on variability of nekton assemblage was hence evaluated by comparing the different model formulations (Table 1) in a hierarchical framework [36,41]. Following the approach of the manyglm function contained in the mvabund software package [74], the inference was carried out at the assemblage Field data were employed in a model framework to identify factors driving nekton variability and then predict assemblage characteristics under scenarios of salinity reduction, following the approach proposed for the restoration of seagrass meadows in the Venice lagoon by Scapin et al [36]. Negative binomial generalised linear models (GLMs) were fitted to biomass of each species contributing to 98% of total assemblage biomass.…”

“…Furthermore, from a methodological perspective, restoration ecology is rapidly starting to incorporate predictive approaches for the evaluation of project performances [35]. Employing forecasting techniques would indeed contribute to provide robust and less uncertain assessments of restoration success, for instance, by predicting in advance the structure of biological communities prior to the end of habitat re-creation actions, [36] hence allowing for an adaptive management of conservation efforts [37,38].…”

“…This paper aims at defining the structure of nekton assemblage to be expected after the restoration of the salinity gradient in an inner, euhaline area of the Venice lagoon, following the predictive approach proposed by Scapin et al [36] for seagrass nekton communities. The work is structured into two phases: (i) an assemblage-level model was calibrated, in order to explain nekton variability with temporal, environmental and habitat factors characterising sites located along natural salinity gradients in the Venice lagoon; (ii) a set of target scenarios were defined in terms of salinity and other environmental factors that are expected at the end of the restoration process.…”