Kate J. Helmstedt

Making informed conservation decisions under climate change is a challenging task for practitioners, since decisions depend on changing environmental conditions and uncertain ecosystem responses to climate change. Given such uncertainties, the best practice to manage natural systems is adaptive management, where decisions dynamically adapt to the response of the ecosystem to previous conservation actions. Although adaptive approaches are optimal, they are also difficult to implement, have high computational costs, and recommend strategies that can be complex to interpret. These factors can hinder their on-ground application. On the other hand, simpler but suboptimal decision models can result in more interpretable recommendations, and might still yield good outcomes for ecosystems. Exploring trade-offs between complex optimal solutions and simpler sub-optimal solutions is essential for maximising conservation impact. In this manuscript, we use value of information theory to help managers simplify their decision-models, while balancing optimality of strategies. Our approach provides modelling recommendations by determining the benefits of modelling non-stationary ecosystem dynamics and the uncertain ecosystem response to climate change. We illustrate our approach on four scenarios inspired from the management of the Great Barrier Reef, Australia, under different climate change trajectories. We find that the two main drivers of the recommended reduction in model complexity are the strength of non-stationarity (e.g. climate change trajectory) and the degree of uncertainty in ecosystem responses to climate change (e.g. uncertainty in the thermal resistance of a coral reef). When non-stationarity is weak, the decision problem can be reduced from a non-stationary to a stationary formulation. Similarly, when uncertainty in the response to climate change is low, this uncertainty can be safely ignored in the decision-making process. Conversely, when non-stationarity is strong and/or uncertainty is high, our approach justifies the need to account for these complexities when making decisions, as simpler approaches would yield poor outcomes. This manuscript guides managers in simplifying a modelling approach to manage ecosystems in the face of climate change. Our protocol can help simplify complex decision problems, allowing to reduce computational costs and enhance interpretability. By finding the balance between simplicity and optimality of models, this work contributes to bridging the gap between complex modelling and on-ground applications.

Monitoring is essential for nature conservation, but many programmes are criticized for lacking purpose. We argue that monitoring delivers impact only when grounded in a clear theory of how activities lead to change. We clarify and categorize 15 distinct reasons to monitor within a theory-of-change framework, outlining how these can guide decisions about where to invest effort. These reasons fall into five groups: basic and applied research aimed at causal evaluation; monitoring integrated with on-ground actions; monitoring to inform policy; monitoring that strengthens enabling conditions for conservation; and curiosity-driven monitoring. Efforts to quantify the benefits of monitoring often focus on narrow, intervention-specific purposes, typically within adaptive management or evidence-based conservation approaches. However, much ecological monitoring serves functions beyond these frameworks. A broader perspective reveals additional, often overlooked, reasons to monitor, especially those that build the enabling conditions required for effective policy and practice. The benefits of these reasons for monitoring have rarely been articulated or quantified. Before designing a monitoring programme, conservation organizations should articulate a theory of change that makes their reasons for monitoring explicit. We provide a checklist of 15 reasons to support transparent logic, intentional design and clear links between monitoring information and improved policy or management outcomes.

The rapid expansion of biological data collection is uneven globally and many remote sites remain understudied. Species distribution models fitted in data‐rich survey areas can be transferred to predict distributions in understudied areas. However, transfers are challenging and often fail due to factors such as differences in conditions between training and projection sites. Integrated species distribution models that harness benefits from multiple data types can improve distribution predictions relative to single‐dataset approaches, but whether data integration improves spatial transferability remains unclear. Here, we evaluate the performance of integrated (PA‐PO), presence‐only (PO) and presence–absence (PA) models in simulated spatial model transfer scenarios that emulate two reasons that transfers fail: environmental dissimilarity of training and projection sites and a hidden spatial process that induces spatial autocorrelation of the species distribution. We found that PA‐PO integrated models outperformed PO and PA models on average during spatial transfer, but the magnitude of the improvement varied among transfer scenarios. Integrated models were better able to predict species distributions at new, environmentally dissimilar, sites than single‐dataset approaches due to lower requirements for extrapolation into novel environments and reduced effects from sampling bias. Spatial autocorrelation from a hidden spatial process was accounted for well by integrated models, leading to modest but notable gains in predictive performance during transfer. Our findings highlight the potential for diverse data sources to be integrated to improve predictions of species distributions in understudied regions. We outline how factors like data quality and transfer conditions can indicate situations when integration could be beneficial for spatial transfer.

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 optimization task when managing dynamic systems, such as threatened ecological systems. Using an adaptive management approach from AI, we find 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 Australia’s Great Barrier Reef as a case study, we demonstrate that the development time limit ranges between 0 to 45 y before surrendering. We also show how characteristics of an ecological 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.

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 Australia’s 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.

Strategies for achieving sustainability in the winegrowing industry require balancing resource investment against the economic outcomes of resultant yields and sale price of the produce. Although there has been much research into forecasting outcomes and resource use, little has been done to illustrate their effects on one another and the consequential economic outcomes. This analysis uses statistical models to observe relationships between resource use, yield, and sale price. The dataset used for this analysis includes data collected for the past 10 years from 1261 vineyards located over a diverse range of Australian winegrowing regions. Yield and sale price were evaluated regarding resource use factors, such as water use and Greenhouse Gas (GHG) emissions. The analysis confirmed a strong relationship between area and resource use, with the overall area of a vineyard and its access to resources significantly determining the upper limit of yield. However, the area was also negatively related to the average sale price of grapes; we find that higher average sale prices were connected to high resource inputs per area rather than to the overall expenditure of resources. Regional and temporal effects on vineyard yield and average sales price were also identified. The analysis highlighted the importance of considering a vineyard’s business goal, region, external pressures, and economies of scale when pursuing higher yields verse higher average sales prices.

Marine invasive species can cause irreparable change in new environments, though not all non‐native species inevitably cause negative impacts to recipient ecosystems. Knowing which non‐native species could establish and have harmful impacts is vital to ensure the efficient use of limited resources for monitoring and surveillance, especially in less accessible regions such as the Southern Ocean. We used ensemble ecosystem modelling to predict an ensemble of plausible future scenarios of introduction and potential establishment of marine non‐native species to a known food web near Casey Station, East Antarctica. These scenarios explore potential impacts of successful invasions by five non‐native species. Non‐native species varied in their capacity to establish in the current food web following a single introduction event, though all could establish in at least some scenarios. Where the non‐native species did establish, their abundances mostly increased by between 10 and 1000 times, though some scenarios resulted in a million‐fold increase. Although most scenarios showed native species abundances only changed within 10% of the initial abundance, the establishment of an invasive species could plausibly cause substantial abundance declines to all native species. Synthesis and applications. This analysis helps us to better understand the potential range of impacts on native species and aids in developing strategies to help prevent or manage their introduction. We highlight the need for rapid detection methods, such as eDNA, to ensure any non‐native species are identified quickly in this ecosystem, particularly for those species we identified as having a high potential for negative impacts and fast population growth.

Disturbances, such as hurricanes, fires, droughts and pest outbreaks, can cause major changes in ecosystem conditions that threaten Nature’s contributions to people (ecosystem services). Climate change is intensifying disturbances, posing risks to ecosystem services. To assess those risks, we develop a flexible, functional trait-based approach to quantify ecological, ecosystem service and economic impacts from disturbance regimes. Our broadly applicable approach integrates knowledge from disturbance ecology and ecosystem service valuation, and we highlight the pitfalls of using either perspective in isolation. We demonstrate our approach by quantifying impacts to timber and recreational enjoyment from extreme windstorms in a midlatitude forest. While we predict large potential losses to these services under historical and future disturbance regimes, common ecological metrics of compositional and biomass stability are inadequate for predicting these impacts. We then provide a roadmap for applying our approach across different social-ecological systems, illustrating the approach for crop pollination, flood hazard mitigation and cultural values from coral reefs—which all face intensifying disturbances. This study highlights and provides tools to address the pressing need to consider disturbances in future ecosystem service assessments.

Aim: The globally significant communities of terrestrial Antarctica face an uncertain future amid growing threats in the region. Emerging data‐driven approaches must be leveraged to predict and understand patterns of biodiversity across the continent. A new comprehensive database of Antarctic biological occurrence records, the Biodiversity of Ice‐free Antarctica Database, will enable such novel fundamental and applied biodiversity modelling. However, there are limitations of assembled occurrence databases that, if unaccounted for, can result in poor model inference and outcomes. We perform a data quality assessment of the new database to highlight its potential and to identify data limitations that must be considered during modelling.Location: Antarctica.Methods: We assessed the coverage of the Biodiversity of Ice‐free Antarctica Database across geographic, environmental, taxonomic, and temporal dimensions at several spatial scales.Results: We demonstrate great potential for the database to improve our understanding of many at risk and poorly known Antarctic functional groups. We also provide evidence for limitations of the database across data dimensions, including low geographic coverage that is biased towards research stations, poor coverage of environmental variation across the landscape, and long periods since records were last collected. The magnitude of these limitations varies substantially by region and spatial scale.Main Conclusions: In combination, data limitations have a range of implications for terrestrial Antarctic modelling, including heightening the risk of model extrapolation. For future use, we recommend prioritising, mitigating, and presenting context‐specific model uncertainty, advancing strategic data collection, and exploiting shared modelling challenges elsewhere in the world to maximise the opportunities for this unique dataset to robustly advance science and conservation in Antarctica.