Development of the coral index, a summary of coral reef resilience as a guide for management

Adaptation of a risk-based framework for evaluating indirect effects of dredging on sensitive habitats near federal navigation channels: An application of the framework to coral reefs at Honolulu Harbor, Hawai'i

In major harbors and ports in the United States and its territories, the US Army Corps of Engineers maintains federal navigation channels in proximity to coral reefs (e.g., Honolulu Harbor, HI; Miami Harbor, FL; Apra Harbor, Guam) and other sensitive habitats. To effectively predict potential adverse impacts from dredging activities near these sensitive habitats, a holistic approach to improve understanding of the pressures on these habitats is needed to foster a more complete prediction of risk drivers. To achieve this, risk-based frameworks that account for the full range of natural and anthropogenic impacts need to be adapted and applied specifically for assessing and managing indirect dredging impacts on sensitive environments. In this article, we address this need by incorporating a drivers-pressures-stressors-condition-response (DPSCR4 ) conceptual framework to broaden a comprehensive conceptual model of the coupled human-ecological system. To help understand these complex interactions, DPSCR4 was applied to evaluate dredging and other unrelated environmental pressures (e.g., terrestrial runoff) in a proof-of-concept dredging project in Honolulu Harbor, Hawai'i, USA, with a focus on the indirect effects of dredge plumes. Particle tracking models and risk-based tools were used to evaluate sediment resuspended during a hypothetical mechanical dredging activity near sensitive coral habitats. Stoplight indicators were developed to predict indirect sediment plume impacts on coral and then compared to exposure modeling results. The strengths and limitations of the approach are presented and the incorporation of the risk framework into environmental management decisions is discussed. Integr Environ Assess Manag 2024;20:547-561. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Response and resilience of karst subterranean estuary communities to precipitation impacts

The impact of meteorological phenomena on ecosystem communities of karst subterranean estuaries (KSEs) remains unknown. KSEs are characterized by vertically stratified groundwater separated by a halocline and host endemic aquatic cave-adapted fauna (stygobionts). In October 2015, 8 days of heavy precipitation caused the first recorded mortality event in the KSE. This event was marked by a halocline shift 5 m deeper. The present study aimed to provide insights into resilience of KSEs faunal communities to temporal shifts in temperature and precipitation. Cave water temperature decreased on average 0.0068°C per mm of accumulated precipitation over 4 days, which can add up to, and surpass, the interannual temperature variation in cases of heavy precipitations. Biological surveys (2012-2021) conducted within cave systems El Aerolito and La Quebrada, in Cozumel, indicated that change in community structure was not detected and stygobionts were resilient; however, marine species inhabiting the caves were impacted. Overall, the faunal community at KSEs remains resilient within short-term meteorological phenomena despite shifts of non-stygobionts.

Macroalgal cover on coral reefs: Spatial and environmental predictors, and decadal trends in the Great Barrier Reef

Macroalgae are an important component of coral reef ecosystems. We identified spatial patterns, environmental drivers and long-term trends of total cover of upright fleshy and calcareous coral reef inhabiting macroalgae in the Great Barrier Reef. The spatial study comprised of one-off surveys of 1257 sites (latitude 11-24°S, coastal to offshore, 0-18 m depth), while the temporal trends analysis was based on 26 years of long-term monitoring data from 93 reefs. Environmental predictors were obtained from in situ data and from the coupled hydrodynamic-biochemical model eReefs. Macroalgae dominated the benthos (≥50% cover) on at least one site of 40.4% of surveyed inshore reefs. Spatially, macroalgal cover increased steeply towards the coast, with latitude away from the equator, and towards shallow (≤3 m) depth. Environmental conditions associated with macroalgal dominance were: high tidal range, wave exposure and irradiance, and low aragonite saturation state, Secchi depth, total alkalinity and temperature. Evidence of space competition between macroalgal cover and hard coral cover was restricted to shallow inshore sites. Temporally, macroalgal cover on inshore and mid-shelf reefs showed some fluctuations, but unlike hard corals they showed no systematic trends. Our extensive empirical data may serve to parameterize ecosystem models, and to refine reef condition indices based on macroalgal data for Pacific coral reefs.

Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth

Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long regrowth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here, we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding and predicting biphasic population growth. The two key components of the framework are as follows: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters and (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences.

Community-specific "desired" states for seagrasses through cycles of loss and recovery

Seagrass habitats provide critical ecosystem services, yet there is ongoing concern over mounting pressures and continuing degradation. Defining a desired state for these habitats is a key step in implementing appropriate management but is often difficult given the challenges of available data and an evaluation of where to set benchmarks. We use more than 20 years of historical seagrass biomass data (1995-2018) for the diverse seagrass communities of Australia's Great Barrier Reef World Heritage Area (GBRWHA) to develop desired state benchmarks. Desired state for seagrass biomass was estimated for 25 of 36 previously defined seagrass communities with the remainder having insufficient data. Desired state varied by more than one order of magnitude between community types and was influenced by the mix of species in the communities and the range of environmental conditions. We identify a historical, decadal-scale cycle of decline with recovery to desired state in coastal intertidal communities. In contrast a number of the estuary and coastal subtidal communities have not recovered to desired state biomass. Understanding a historical context is critically important for setting benchmarks and making informed management decisions on the present state of seagrass in the GBRWHA. The approach we have developed is scalable for monitoring, management and assessment of pressures for other management areas and for other jurisdictions. Our results guide conservation planning through prioritization of the at-risk seagrass communities that are continuing to fall below their desired state.

Parameter identifiability and model selection for sigmoid population growth models

Sigmoid growth models, such as the logistic, Gompertz and Richards' models, are widely used to study population dynamics ranging from microscopic populations of cancer cells, to continental-scale human populations. Fundamental questions about model selection and parameter estimation are critical if these models are to be used to make practical inferences. However, the question of parameter identifiability - whether a data set contains sufficient information to give unique or sufficiently precise parameter estimates - is often overlooked. We use a profile-likelihood approach to explore practical parameter identifiability using data describing the re-growth of hard coral. With this approach, we explore the relationship between parameter identifiability and model misspecification, finding that the logistic growth model does not suffer identifiability issues for the type of data we consider whereas the Gompertz and Richards' models encounter practical non-identifiability issues. This analysis of parameter identifiability and model selection is important because different growth models are in biological modelling without necessarily considering whether parameters are identifiable. Standard practices that do not consider parameter identifiability can lead to unreliable or imprecise parameter estimates and potentially misleading mechanistic interpretations. For example, using the Gompertz model, the estimate of the time scale of coral re-growth is 625 days when we estimate the initial density from the data, whereas it is 1429 days using a more standard approach where variability in the initial density is ignored. While tools developed here focus on three standard sigmoid growth models only, our theoretical developments are applicable to any sigmoid growth model and any appropriate data set. MATLAB implementations of all software are available on GitHub.

The Fucales Index: A new tool for monitoring subtidal rocky habitats, and its application to an Atlantic bay subjected to nuclear power plant's effluents

Ecological indices are useful tools for environmental managers to monitor and detect changes caused by natural or anthropogenic disturbances. Despite the fact that descriptors of Fucales species are often included in indices for evaluating changes in coastal marine habitats, there is no index based solely on Fucales abundance. This study proposes the Fucales Index (FI), based on four classes of abundance of a selected Fucales species. The ability of FI to detect changes in the abundance of Sargassum sp. was tested in a Brazilian bay that is subjected to the effluent plume from a nuclear power plant. FI was significantly different when comparing areas inside and outside of the plume area, and it increased with increasing distance from the source of the disturbance. These findings suggest that FI is a suitable tool for assessing the effect of an effluent plume and potentially of other disturbances on rocky shores hosting Fucales.