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Jeroen Gerritsen

Publications and source records attributed to Jeroen Gerritsen.

5 recordsLinked to original sources
USGS2022

A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules

As coral reef condition and sustainability continue to decline worldwide, losses of critical habitat and their ecosystem services have generated an urgency to understand and communicate reef response to management actions, environmental contamination, and natural disasters. Increasingly, coral reef protection and restoration programs emphasize the need for robust assessment tools for protecting high-quality waters and establishing conservation goals. Of equal importance is the need to communicate assessment results to stakeholders, beneficiaries, and the public so that environmental consequences of decisions are understood. The Biological Condition (BCG) model provides a structure to evaluate the condition of a coral reef in increments of change along a gradient of human disturbance. Communication of incremental change, regardless of direction, is important for decision makers and the public to better understand what is gained or lost depending on what actions are taken. We developed a narrative (qualitative) Biological Condition Gradient (BCG) from the consensus of a diverse expert panel to provide a framework for coral reefs in US Caribbean Territories. The model uses narrative descriptions of biological attributes for benthic organisms to evaluate reefs relative to undisturbed or minimally disturbed conditions. Using expert elicitation, narrative decision rules were proposed and deliberated to discriminate among six levels of change along a gradient of increasing anthropogenic stress. Narrative rules for each of the BCG levels are presented to facilitate the evaluation of benthic communities in coral reefs and provide specific narrative features to detect changes in coral reef condition and biological integrity. The BCG model can be used in the absence of numeric, or quantitative metrics, to evaluate actions that may encroach on coral reef ecosystems, manage endangered species habitat, and develop and implement management plans for marine protected areas, watersheds, and coastal zones. The narrative BCG model is a defensible model and communication tool that translates scientific results so the nontechnical person can understand and support both regulatory and non-regulatory water quality and natural resource programs.

Puerto Rico, U.S. Virgin Islands↗
USGS2020

Characterizing benthic macroinvertebrate and algal biological condition gradient models for California wadeable Streams, USA

The Biological Condition Gradient (BCG) is a conceptual model that describes changes in aquatic communities under increasing levels of anthropogenic stress. The BCG helps decision-makers connect narrative water quality goals (e.g., maintenance of natural structure and function) to quantitative measures of ecological condition by linking index thresholds based on statistical distributions (e.g., percentiles of reference distributions) to expert descriptions of changes in biological condition along disturbance gradients. As a result, the BCG may be more meaningful to managers and the public than indices alone. To develop a BCG model, biological response to stress is divided into 6 levels of condition, represented as changes in biological structure (abundance and diversity of pollution sensitive versus tolerant taxa) and function. We developed benthic macroinvertebrate (BMI) and algal BCG models for California perennial wadeable streams to support interpretation of percentiles of reference-based thresholds for bioassessment indices (i.e., the California Stream Condition Index [CSCI] for BMI and the Algal Stream Condition Index [ASCI] for diatoms and soft-bodied algae). Two panels (one of BMI ecologists and the other of algal ecologists) each calibrated a general BCG model to California wadeable streams by first assigning taxa to specific tolerance and sensitivity attributes, and then independently assigning test samples (264 BMI and 248 algae samples) to BCG Levels 1–6. Consensus on the assignments was developed within each assemblage panel using a modified Delphi method. Panels then developed detailed narratives of changes in BMI and algal taxa that correspond to the 6 BCG levels. Consensus among experts was high, with 81% and 82% expert agreement within 0.5 units of assigned BCG level for BMIs and algae, respectively. According to both BCG models, the 10th percentiles index scores at reference sites corresponded to a BCG Level 3, suggesting that this type of threshold would protect against moderate changes in structure and function while allowing loss of some sensitive taxa. The BCG provides a framework to interpret changes in aquatic biological condition along a gradient of stress. The resulting relationship between index scores and BCG levels and narratives can help decision-makers select thresholds and communicate how these values protect aquatic life use goals.

California↗
USGS2012

Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach

Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable. In realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.

Scientific Investigations Report↗