Biology subjects

Helmstedt, K.

Publications and source records attributed to Helmstedt, K..

3 recordsLinked to original sources

bioRxiv2026

Parameter estimation and identifiability analysis of stability and tipping points in potentially bistable ecosystems

Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is crossed, the system may degrade and recovery can be difficult, making early detection essential for effective ecosystem management. Predicting these tipping points requires models that exhibit bistability, representing systems that can exist in two alternative stable states under identical environmental conditions. A key question is whether standard ecological monitoring data can be used to identify bistability and accurately estimate tipping points. Using the Carpenter model of lake eutrophication, which expresses bistability between clear and polluted water states, we generate synthetic data under known stability regimes. Profile likelihood analysis is then applied to assess parameter identifiability and detect system stability and tipping points. Our results show that standard monitoring data do not always provide sufficient information to distinguish bistable from stable regimes. Importantly, bistability and tipping points become practically identifiable only when data are collected very close to the tipping point.

ecology↗

bioRxiv2024

Developing new technologies to protect ecosystems: planning with adaptive management

Technology development is an essential investment for policymakers to address contemporary global crises, including climate change, biodiversity loss, the energy transition, and emergent infectious diseases. However, investing limited resources in the development of new technologies is risky. The research and development process is unpredictable, with unknown timelines and outcomes. In addition, even after successful development, the effects of deploying a new technology remain uncertain. When confronted with these uncertainties, policymakers must determine how long they should allocate resources to developing new technologies. Informed decisions require anticipating possible successes and failures of both technology development and deployment, which is a challenging optimisation task when managing dynamic systems, such as threatened ecological systems. Using an adaptive management approach from Artificial Intelligence, we discover a time limit new technologies should be developed for, which balances costs, benefits, and uncertainties during development and deployment. We extract clear and transparent general rules for investing in new technologies, building on an analytical approximation. Using Australias Great Barrier Reef as a case study, we demonstrate how characteristics of the managed system influence the optimal investment strategy. Our approach can inform the development of new technologies in multiple domains including biodiversity conservation, public health, energy production, and the technology industry more broadly. SignificanceTechnology development is essential to address the crises our world faces, such as ecosystem collapse. With limited resources, policymakers must decide whether to invest in developing new technologies and, if ever, when to stop. Informed decisions require anticipating possible failures of both technology development and deployment, a challenging task when dealing with changing systems. Using an Artificial Intelligence approach, we find a time limit for technology development that depends on characteristics of the managed ecosystem. This work can guide technology investments in many domains such as biodiversity conservation, epidemiology, energy production and the technology industry more broadly.

ecology↗

bioRxiv2024

Optimising crown-of-thorns starfish control effort on the Great Barrier Reef

Outbreaks of crown-of-thorns starfish Acanthaster planci (COTS), a disruptive coral-eating predator, are responsible for almost half of total coral cover loss on Australias Great Barrier Reef. As the pressures of climate change continue to intensify the frequency and severity of disturbance events such as cyclones and coral bleaching, efficiently managing COTS outbreaks is essential for reef protection. We aim to understand how the spatial distribution and intensity of crown-of-thorns starfish control - specifically manual culling of COTS by human divers - can impact coral cover on the GBR. We construct a metapopulation model based on a predator-prey model with larval dispersal and removal of crown-of-thorns starfish to simulate and compare spatial control strategies. When outbreaks begin on reefs between Cairns and Cooktown, we found the best strategy is to target those reefs at the source of the COTS outbreak. Increasing the spatial spread of control results in a larger spatial area protected across the GBR, but a lower total coral cover on the GBR. Our findings suggest that carefully targeting future control by considering larval connectivity patterns and spatial control strategies could lead to more efficient crown-of-thorns management. With the increasing pressures of climate change, any efficiency gains in reef management will prove beneficial for the Great Barrier Reef.

ecology↗