How models can support ecosystem-based management of coral reefs

Despite the importance of coral reef ecosystems to the social and economic welfare of coastal communities, the condition of these marine ecosystems have generally degraded over the past decades. With an increased knowledge of coral reef ecosystem processes and a rise in computer power, dynamic models are useful tools in assessing the synergistic effects of local and global stressors on ecosystem functions. We review representative approaches for dynamically modeling coral reef ecosystems and categorize them as minimal, intermediate and complex models. The categorization was based on the leading principle for model development and their level of realism and process detail. This review aims to improve the knowledge of concurrent approaches in coral reef ecosystem modeling and highlights the importance of choosing an appropriate approach based on the type of question(s) to be answered. We contend that minimal and intermediate models are generally valuable tools to assess the response of key states to main stressors and, hence, contribute to understanding ecological surprises. As has been shown in freshwater resources management, insight into these conceptual relations profoundly influences how natural resource managers perceive their systems and how they manage ecosystem recovery. We argue that adaptive resource management requires integrated thinking and decision support, which demands a diversity of modeling approaches. Integration can be achieved through complimentary use of models or through integrated models that systemically combine all relevant aspects in one model. Such whole-of-system models can be useful tools for quantitatively evaluating scenarios. These models allow an assessment of the interactive effects of multiple stressors on various, potentially conflicting, management objectives. All models simplify reality and, as such, have their weaknesses. While minimal models lack multidimensionality, system models are likely difficult to interpret as they require many efforts to decipher the numerous interactions and feedback loops. Given the breadth of questions to be tackled when dealing with coral reefs, the best practice approach uses multiple model types and thus benefits from the strength of different models types

Understanding tilapia mortalities and fish health management in Lake Volta: a systematic approach

Unusual fish mortalities in aquaculture threaten global food security and carry significant socio-economic burdens. In 2018, Nile tilapia (Oreochromis niloticus) suffered unusual patterns of mortalities, attributed to disease-causing agents in Lake Volta cage aquaculture. In recent times, disease investigations have shifted from single to consideration of multiple factors to understand the puzzling range of causal risk factors. This study therefore aimed at expanding on tilapia mortality risk factors, while documenting fish health and Lake Volta management practices for sustainable aquaculture. We interviewed relevant aquaculture stakeholders operating on Lake Volta and conducted thematic analysis on their responses to map out mortality risk factors and management practices. The identified risk factors were conceptualized in established models of causation web and Social-Ecological System to explain the practical significance of the findings. The results showed that the risk factors of tilapia mortalities are a combination of pathogens and non-infectious factors mediated by weak law enforcement. The results further suggested mortality reinforcing mechanisms through the horizontal transmission of pathogens, namely, Streptococcus agalactiae and Infectious Spleen and Kidney Necrosis Virus. Moreover, the recognition of weak enforcement as a possible factor reinforcing human activities is a non-infectious route that can be deleterious to fish health. Health management practices comprised phytotherapy, vaccination, heat shock treatment, biological controls, and best husbandry practices. Lake management involves creating a waterfront buffer of 85.34 m, surveillance, and executing the framework guiding aquaculture development on the Lake. The findings are suggestive of complementary quantitative studies that augment the qualitative evidence herein. Such follow up studies can disclose precise mortality risk factors to inform policy directives and effective remedial strategies that can secure fish and lake health

Loss of Earth system resilience during early Eocene transient global warming events

Superimposed on long-term late Paleocene–early Eocene warming (~59 to 52 million years ago), Earth’s climate experienced a series of abrupt perturbations, characterized by massive carbon input into the ocean-atmosphere system and global warming. Here, we examine the three most punctuated events of this period, the Paleocene-Eocene Thermal Maximum and Eocene Thermal Maximum 2 and 3, to probe whether they were initiated by climate-driven carbon cycle tipping points. Specifically, we analyze the dynamics of climate and carbon cycle indicators acquired from marine sediments to detect changes in Earth system resilience and to identify positive feedbacks. Our analyses suggest a loss of Earth system resilience toward all three events. Moreover, dynamic convergent cross mapping reveals intensifying coupling between the carbon cycle and climate during the long-term warming trend, supporting increasingly dominant climate forcing of carbon cycle dynamics during the Early Eocene Climatic Optimum when these recurrent global warming events became more frequent

Estimation of Resilience Parameters Following LPS Injection Based on Activity Measured With Computer Vision

Resilience could be referred to as the animal’s ability to successfully adapt to a challenge. This is typically displayed by a quick return to initial metabolic or activity levels and behaviors. Pigs have distinct diurnal activity patterns. Deviations from these patterns could potentially be utilized to quantify resilience. However, human observations of activity are labor intensive and not feasible in practice on a large scale. In this study, we show the use of a computer vision tracking algorithm to quantify resilience based on activity individual patterns following a lipopolysaccharide (LPS) challenge, which induced a sickness response. We followed 121 individual pigs housed in barren or enriched housing systems, as previous work suggests an impact of housing on resilience, for eight days. The enriched housing consisted of delayed weaning in a group farrowing system and extra space compared with the barren pens and environmental enrichment. Enriched housed pigs were more active pre-injection of LPS, especially during peak activity times, than barren housed pigs (49.4 ± 9.9 vs. 39.1 ± 5.0 meter/hour). Four pigs per pen received an LPS injection and two pigs a saline injection. LPS injected animals were more likely to show a dip in activity than controls (86% vs 17%). Duration and Area Under the Curve (AUC) of the dip were not affected by housing. However, pigs with the same AUC could have a long and shallow dip or a steep and short dip. Therefore the AUC:duration ratio was calculated, and enriched housed pigs had a higher AUC:duration ratio compared to barren housed pigs (9244.1 ± 5429.8 vs 5919.6 ± 4566.1). Enriched housed pigs might therefore have a different strategy to cope with an LPS sickness challenge. However, more research on this strategy and the use of activity to quantify resilience and its relationship to physiological parameters is therefore needed

Critical slowing down as early warning for the onset and termination of depression

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression

Stability and Fluctuations in Complex Ecological Systems

From 08-12 August, 2022, 32 individuals participated in a workshop, Stability and Fluctuations in Complex Ecological Systems, at the Lorentz Center, located in Leiden, The Netherlands. An interdisciplinary dialogue between ecologists, mathematicians, and physicists provided a foundation of important problems to consider over the next 5-10 years. This paper outlines eight areas including (1) improving our understanding of the effect of scale, both temporal and spatial, for both deterministic and stochastic problems; (2) clarifying the different terminologies and definitions used in different scientific fields; (3) developing a comprehensive set of data analysis techniques arising from different fields but which can be used together to improve our understanding of existing data sets; (4) having theoreticians/computational scientists collaborate closely with empirical ecologists to determine what new data should be collected; (5) improving our knowledge of how to protect and/or restore ecosystems; (6) incorporating socio-economic effects into models of ecosystems; (7) improving our understanding of the role of deterministic and stochastic fluctuations; (8) studying the current state of biodiversity at the functional level, taxa level and genome level.Comment: 22 page

Quantifying resilience of humans and other animals

All life requires the capacity to recover from challenges that are as inevitable as they are unpredictable. Understanding this resilience is essential for managing the health of humans and their livestock. It has long been difficult to quantify resilience directly, forcing practitioners to rely on indirect static indicators of health. However, measurements from wearable electronics and other sources now allow us to analyze the dynamics of physiology and behavior with unsurpassed resolution. The resulting flood of data coincides with the emergence of novel analytical tools for estimating resilience from the pattern of micro-recoveries observed in natural time series. Such dynamic indicators of resilience (DIORs) may be used to monitor the risk of systemic failure across systems ranging from organs to entire organisms. These tools invite a fundamental rethink of our approach to the adaptive management of health and resilience

Multiple feedbacks and the prevalence of alternate stable states on coral reefs

The prevalence of alternate stable states on coral reefs has been disputed, although there is universal agreement that many reefs have experienced substantial losses of coral cover. Alternate stable states require a strong positive feedback that causes self-reinforcing runaway change when a threshold is passed. Here we use a simple model of the dynamics of corals, macroalgae and herbivores to illustrate that even weak positive feedbacks that individually cannot lead to alternate stable states can nonetheless do so if they act in concert and reinforce each other. Since the strength of feedbacks varies over time and space, our results imply that we should not reject or accept the general hypothesis that alternate stable states occur in coral reefs. Instead, it is plausible that shifts between alternate stable states can occur sporadically, or on some reefs but not others depending on local conditions. Therefore, we should aim at a better mechanistic understanding of when and why alternate stable states may occur. Our modelling results point to an urgent need to recognize, quantify, and understand feedbacks, and to reorient management interventions to focus more on the mechanisms that cause abrupt transitions between alternate states.</p

Critical size of a local disturbance and the speed of a travelling wave as a function of the maximal mortality rate <i>c</i>.

<p><i>(a)</i> On an infinitely sized landscape, disturbances smaller than the critical size <i>Δx</i> (in m) are repaired, while larger disturbances will initiate a propagating wave that travels through the landscape with <i>(b)</i> a constant wave speed (in m d⁻¹). The thick dashed line represents the Maxwell point. The thin dashed lines represent the two fold bifurcations in a non-spatial system. Left of the Maxwell point the entire landscape was initially set to the low biomass state, and the disturbance was set to the high biomass state. Right of the Maxwell point the landscape was initially set to the high biomass state, and the disturbance was set to the low biomass state (indicated by the small upper panels). In this model, an <i>n</i>-fold increase in diffusion rate leads to a </p><p></p><p></p><p></p><p><mi>n</mi></p><p></p><p></p><p></p>-fold increase in both critical disturbance size and wave speed.<p></p

Foreseeing the future of mutualistic communities beyond collapse

Changing conditions may lead to sudden shifts in the state of ecosystems when critical thresholds are passed. Some well-studied drivers of such transitions lead to predictable outcomes such as a turbid lake or a degraded landscape. Many ecosystems are, however, complex systems of many interacting species. While detecting upcoming transitions in such systems is challenging, predicting what comes after a critical transition is terra incognita altogether. The problem is that complex ecosystems may shift to many different, alternative states. Whether an impending transition has minor, positive or catastrophic effects is thus unclear. Some systems may, however, behave more predictably than others. The dynamics of mutualistic communities can be expected to be relatively simple, because delayed negative feedbacks leading to oscillatory or other complex dynamics are weak. Here, we address the question of whether this relative simplicity allows us to foresee a community’s future state. As a case study, we use a model of a bipartite mutualistic network and show that a network’s post-transition state is indicated by the way in which a system recovers from minor disturbances. Similar results obtained with a unipartite model of facilitation suggest that our results are of relevance to a wide range of mutualistic systems