Optimizing facility location, sizing, and growth time for a cultivated resource: A case study in coral aquaculture

Production of cultivated resources require additional planning that takes growth time into account. We formulate a mathematical programming model to determine the optimal location and sizing of growth facilities, impacted by resource survival rate as a function of its growth time. Our method informs strategic decisions regarding the number, location, and sizing of facilities, as well as operational decisions of optimal growth time for a cultivated resource in a facility to minimize total costs. We solve this facility location and sizing problem in the context of coral aquaculture for large-scale reef restoration using a two-stage algorithm and a linear mixed-integer solver. We assess growth time in a facility in terms of its impact on survival (post-deployment) considering growth quantity requirements and growth facility production constraints. We explore the sensitivity of optimal facility number, location, and sizing to changes in the geographic distribution of demand and cost parameters computationally. Results show that the relationship between growth time and survival is critical to optimizing operational decisions for grown resources. These results inform the value of data certainty to optimize the logistics of coral aquaculture production.

Defining performance thresholds for effective management of biodiversity within protected areas

Performance thresholds are an important tool for determining successful conservation outcomes. They provide an objective means of defining good ecological condition and have been endorsed as an essential part of best practice in protected area (PA) management within the International Union for the Conservation of Nature Green List of Protected and Conserved Areas Standard. With a growing number of PAs attaining Green List status globally, thresholds developed by PAs on the Green List present an excellent resource with which to identify the attributes of well-defined performance thresholds. We examined 349 thresholds associated with PAs on the Green List to determine whether they were specific and measurable (i.e., factors recognized as essential for setting well-defined targets). We assessed whether thresholds were defined quantitatively and whether definitions included ambiguous terms (e.g., stable numbers). We identified six different ways thresholds were expressed and found that many thresholds were expressed as management objectives, rather than ecological condition thresholds, although this trend improved over time. Approximately one-half of the performance thresholds lacked the necessary specificity to delineate successful outcomes. Our results enabled us to develop a checklist of information required to set robust performance thresholds. Recommendations include that thresholds should be quantitatively defined, including quantitative estimates of the limits of acceptable change (LAC) around the target condition. To ensure transparency, a rationale and associated evidence should be provided to support the threshold and the LAC. When accompanied by a rationale and quantitative estimate of the current condition of the value, unambiguously defined thresholds with a quantitative LAC provide an objective means of demonstrating that successful conservation outcomes have been achieved. These recommendations will help conservation managers apply the Green List Standard and improve the measurement of conservation outcomes more broadly.

Lessons from other disciplines for setting management thresholds for biodiversity conservation

Successful, state-dependent management, in which the goal of management is to maintain a system in a desired state, involves defining the boundaries between different states. Once these boundaries have been defined, managers require a strategic action plan with thresholds that initiate management interventions to either maintain or return the system to a desired state. This approach to management is widely used across diverse industries from agriculture, to medicine, to information technology, but it has only been adopted in conservation management relatively recently. Conservation practitioners have expressed a willingness to integrate this structured approach in their management systems, but they have also voiced concerns, including lack of a robust process for doing so. Given the widespread use of state-dependent management in other fields, we conducted an extensive review of the literature on threshold-based management to gain insight into how and where it is applied and identify potential lessons for conservation management. We identified 22 industries using 75 different methods for setting management thresholds in 843 studies. Methods spanned six broad approaches, including expert driven, statistical, predictive, optimization, experimental, and artificial intelligence methods. The objectives of each of these studies influenced the approaches used, including the methods for setting thresholds and selecting actions, and the number of thresholds set. The role of value judgments in setting thresholds was clear; studies across all industries frequently involved experts in setting thresholds, often accompanied by computational tools to simulate the consequences of proposed thresholds under different conditions. Of the 30 conservation studies examined, two-thirds used expert-driven methods, consistent with prior evidence that experience-based information often drives conservation management decisions. The methods we identified from other disciplines could help conservation decision makers set thresholds for management interventions in different contexts, linking monitoring to management actions and ensuring that conservation interventions are timely and effective.

Two species, one island: Retrospective analysis of threatened fauna translocations with divergent outcomes

Translocations are globally a popular tool used with the intention of improving threatened species conservation and re-establishing ecosystem function. While practitioners strive for successful outcomes the failure rate of translocations continues to be high. We demonstrate how predictive modelling can contribute to more informed decision making and hence potentially improve the success rate of translocation programs. Two species, the Djoongari (Shark Bay mouse) Pseudomys fieldi and the golden bandicoot Isoodon auratus barrowensis, were introduced independently to Doole Island in the Exmouth Gulf of Western Australia. We used population viability analysis to critique the outcomes of these translocations and provide an example of how this tool can be incorporated with expert knowledge to predict likely outcomes of translocations. Djoongari did not establish on the island after seven translocations over nine years, while golden bandicoots established a population after just one release event. Retrospective population viability analysis (of data that was unavailable prior to the translocations) predicted and clarified the reasons behind the outcomes of both translocations. Golden bandicoots have considerably higher demographic plasticity than Djoongari, which were never likely to establish on the island. We conclude that the failure of the Djoongari translocation was due to interactions between sparse habitat, native predators and cyclonic storm surges, whereas golden bandicoots have demonstrated habitat flexibility and an ability to recover from multiple natural disasters. As a result we (1) remind conservation planners of the importance of quantifying likely refuges and habitat availability at release sites, (2) suggest practitioners consider how different threats (including natural disasters) may interact at potential release sites and (3) advocate for the incorporation of predictive modelling during the planning stages of translocations, particularly for conservation introductions where no precedent exists for the species’ survival at a particular location.