A new roadmap for quantitative methodologies of Environmental Impact Assessment (EIA) is proposed, using an ecosystem-based approach. EIA recommendations are currently based on case-by-case rankings, distant from statistical methodologies, and ecological ideas that lack proof of generality or predictive capacities. These qualitative approaches ignore process dynamics, scales of variations and interdependencies and are unable to address societal demands to link socio-economic and ecological processes (e.g. population dynamics). We propose to re-focus EIA around the systemic formulation of interactions between organisms (organized in populations and communities) and their environments but inserted within a strict statistical framework. A systemic formulation allows scenarios to be built that simulate impacts on chosen receptors. To illustrate the approach, we design a minimum ecosystem model that demonstrates nontrivial effects and complex responses to environmental changes and validated with case study. We suggest that an Ecosystem-Based EIA—in which the socio-economic system is an evolving driver of the ecological one—is more promising than a socio-economic-ecological system where all variables are treated as equal. This refocuses the debate on cause-and-effect, processes, identification of essential portable variables, and allows for quantitative comparisons between projects, which is critical in cumulative effects determinations.

In this review, the use of environmental DNA (eDNA) within Environmental Impact Assessment (EIA) is evaluated. EIA documents provide information required by regulators to evaluate the potential impact of a development project. Currently eDNA is being incorporated into biodiversity assessments as a complementary method for detecting rare, endangered or invasive species. However, questions have been raised regarding the maturity of the field and the suitability of eDNA information as evidence for EIA. Several key issues are identified for eDNA information within a generic EIA framework for marine environments. First, it is challenging to define the sampling unit and optimal sampling strategy for eDNA with respect to the project area and potential impact receptor. Second, eDNA assay validation protocols are preliminary at this time. Third, there are statistical issues around the probability of obtaining both false positives (identification of taxa that are not present) and false negatives (non-detection of taxa that are present) in results. At a minimum, an EIA must quantify the uncertainty in presence/absence estimates by combining series of Bernoulli trials with ad hoc occupancy models. Finally, the fate and transport of DNA fragments is largely unknown in environmental systems. Shedding dynamics, biogeochemical and physical processes that influence DNA fragments must be better understood to be able to link an eDNA signal with the receptor’s state. The biggest challenge is that eDNA is a proxy for the receptor and not a direct measure of presence. Nonetheless, as more actors enter the field, technological solutions are likely to emerge for these issues. Environmental DNA already shows great promise for baseline descriptions of the presence of species surrounding a project and can aid in the identification of potential receptors for EIA monitoring using other methods.

This viewpoint article examines Environmental Impact Assessment (EIA) practices in developed and transitioning nations, identifies weaknesses, and proposes a new quantitative approach. The literature indicates that there exists little to no standardization in EIA practice, transitioning nations rely on weak scientific impact analyses, and the establishment of baseline conditions is generally missing. The more fundamental issue is that the "receptor"-based approach leads to a qualitative and subjective EIA, as it does not adequately integrate the full measure of the complexity of ecosystems, ongoing project risks, and cumulative impacts. We propose the application of a new framework that aims to ensure full life cycle assessment of impacts applicable to any EIA process, within any jurisdictional context. System-Based EIA (SBEIA) is based on modeling to predict changes and rests on data analysis with a statistically rigorous approach to assess impacts. This global approach uses technologies and methodologies that are typically applied to characterize ecosystem structure and functioning, including remote sensing, modeling, and in situ monitoring. The aim of this approach is to provide a method that can produce quantifiable reproducible values of impact and risk and move EIA towards its substantive goal of sustainable development. The adoption of this approach would provide a better evaluation of economic costs and benefits for all stakeholders.

A cost-effective technology has emerged which combines multispectral sensors mounted on Unmanned Aerial Vehicles (UAVs). This technology has a promising potential for monitoring water quality in coastal environments. Our study aimed at evaluating this technology to infer the spatial distribution of chlorophyll a concentration [Chl-a] (in µg·L−1) and turbidity (FNU) in surface waters. The multispectral sensor measured reflectance at 4 distinct wavelength bands centered on 448 nm, 494 nm, 550 nm and 675 nm, hence providing 4 datasets {R(448), R(494), R(550), R(675)}. We investigated the potential of estimating [Chl-a] and turbidity based on reflectance ratios and indexes calculated from two different wavelength bands. The calibration functions were formulated based on the property that any of the reflectance measurements was linearly correlated to any other one. The calibration was performed from 35 measurements of reflectance, [Chl-a] and turbidity collected in seven sites in the U.K. between May and August 2017. Two calibration functions derived from the index δ=(R(550) − R(448))/(R(550) + R(448)) presented the best fit and explained 78% of the total variance for [Chl-a] and 74% for turbidity measurements, respectively. Calibration functions were then inversed to estimate [Chl-a] and turbidity from reflectance measurements. Finally, we performed a validation test using independent measurements from three sites in France, in July 2017. The resulting maps show a pattern with higher [Chl-a] in lower turbidity areas. However, discrepancies between the observed and re-calculated values and difficulties in validating low turbidity values suggest that site-specific calibrations should be performed at each investigated location.

A new roadmap for quantitative methodologies of Environmental Impact Assessment (EIA) is proposed, using an ecosystem-based approach. EIA recommendations are currently based on case-by-case rankings, distant from statistical methodologies, and based on ecological ideas that lack proof of generality or predictive capacities. These qualitative approaches ignore process dynamics, scales of variations and interdependencies and are unable to address societal demands to link socio-economic and ecological processes (e.g. population dynamics). We propose to re-focus EIA around the systemic formulation of interactions between organisms (organized in populations and communities) and their environments but inserted within a strict statistical framework. A systemic formulation allows scenarios to be built that simulate impacts on chosen receptors. To illustrate the approach, we design a minimum ecosystem model that demonstrates non-trivial effects and complex responses to environmental changes. We suggest further that an Ecosystem-Based EIA - in which the socio-economic system is an evolving driver of the ecological one - is more promising than a socio-economic-ecological system where all variables are treated as equal. This refocuses the debate on cause-and-effect, processes, identification of essential portable variables, and a potential for quantitative comparisons between projects, which is important in cumulative effects determinations.

Early detection of environmental disturbances affecting shellfish stock condition is highly desirable for aquaculture activities. In this article, a new biophysical model-based early warning system (EWS) is described, that assesses bivalve stock condition by diagnosing signs of persistent physiological dysfunctioning. The biophysical model represents valve gape dynamics, controlled by active contractions of the adductor muscle countering the passive action of the hinge ligament; the dynamics combine continuous convergence to a steady-state interspersed with discrete closing events. A null simulation was introduced to describe undisturbed conditions. The diagnostic compares valve gape measurements and simulations. Indicators are inferred from the model parameters, and disturbances are assessed when their estimates deviate from their null distribution. Instead of focusing only on discrete events, our EWS exploits the complete observed dynamics within successive time intervals defined by the variation scales. When applied to a valvometry data series, collected in controlled conditions from scallops (Pecten maximus), the EWS indicated that one among four individuals exhibited signs its physiological condition was degrading. This was detected neither during experiments nor during the initial data analysis, suggesting the utility of an approach that quantifies physiological mechanisms underlying functional responses. Practical implementations of biological-EWS at farming sites are then discussed.

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A model was developed to forecast and compare changes in species presence assessed with either eDNA or traditional observations. We use it to explore how ecosystem conditions could affect the suitability of eDNA for Environmental Impact Assessment. First, a deterministic model simulated the dynamics of the impacted population (called “receptor” in EIA) and their shed DNA fragment concentrations. Second, random distributions of receptor organisms and eDNA fragment quantities at steady state were simulated within the impacted spatial domain (called “project area”). Then, simple random samplings were performed for both the receptor and eDNA. Third, post‐sampling processes (eDNA extraction, amplification, and analysis) were simulated to estimate the taxon detection probability. Fourth, we simulated an impact by modifying the growth, mortality, and mobility (null, passive, and active) parameters of the receptor taxon. eDNA detection probability curves were then estimated for a range of environmental sample volumes by fitting a Weibull cumulative distribution function. A F‐like statistic compared detection curves before and after impact. Statistically significant differences were detected with eDNA in impact scenarios where receptor taxon growth rate decreased and receptor mobility was null or passive. In scenarios where the project area accumulates DNA shed from multiple categories of the same taxon (e.g., from dead organisms if mortality increased or when individuals can cross project area boundaries), it is difficult to assess impact. Our study shows that results obtained from eDNA sampling will not always agree with an impact classically assessed on a receptor population. One reason is that sources of the total eDNA pool are not identified. The modeling highlights the need: to do preliminary testing of sample sizes, to develop new approaches that will identify sources from the pool of extracted DNA, and to improve descriptions of the ecogeochemical processes required to forecast shed DNA reactivity.