Vivitskaia J. Tulloch

Achieving the triple bottom line in the face of inherent trade-offs among social equity, economic return, and conservation

Triple–bottom-line outcomes from resource management and conservation, where conservation goals and equity in social outcomes are maximized while overall costs are minimized, remain a highly sought-after ideal. However, despite widespread recognition of the importance that equitable distribution of benefits or costs across society can play in conservation success, little formal theory exists for how to explicitly incorporate equity into conservation planning and prioritization. Here, we develop that theory and implement it for three very different case studies in California (United States), Raja Ampat (Indonesia), and the wider Coral Triangle region (Southeast Asia). We show that equity tends to trade off nonlinearly with the potential to achieve conservation objectives, such that similar conservation outcomes can be possible with greater equity, to a point. However, these case studies also produce a range of trade-off typologies between equity and conservation, depending on how one defines and measures social equity, including direct (linear) and no trade-off. Important gaps remain in our understanding, most notably how equity influences probability of conservation success, in turn affecting the actual ability to achieve conservation objectives. Results here provide an important foundation for moving the science and practice of conservation planning—and broader spatial planning in general—toward more consistently achieving efficient, equitable, and effective outcomes.

Why do we map threats? Linking threat mapping with actions to make better conservation decisions

Spatial representations of threatening processes – “threat maps” – can identify where biodiversity is at risk, and are often used to identify priority locations for conservation. In doing so, decision makers are prone to making errors, either by assuming that the level of threat dictates spatial priorities for action or by relying primarily on the location of mapped threats to choose possible actions. We show that threat mapping can be a useful tool when incorporated within a transparent and repeatable structured decision-making (SDM) process. SDM ensures transparent and defendable conservation decisions by linking objectives to biodiversity outcomes, and by considering constraints, consequences of actions, and uncertainty. If used to make conservation decisions, threat maps are best developed with an understanding of how species respond to actions that mitigate threats. This approach will ensure that conservation actions are prioritized where they are most cost-effective or have the greatest impact, rather than where threat levels are highest.

Polar lessons learned: Long-term management based on shared threats in Arctic and Antarctic environments

The Arctic and Antarctic polar regions are subject to multiple environmental threats, arising from both local and ex-situ human activities. We review the major threats to polar ecosystems including the principal stressor, climate change, which interacts with and exacerbates other threats such as pollution, fisheries overexploitation, and the establishment and spread of invasive species. Given the lack of progress in reducing global atmospheric greenhouse-gas emissions, we suggest that managing the threats that interact synergistically with climate change, and that are potentially more tractable, is all the more important in the short to medium term for polar conservation. We show how evidence-based lessons learned from scientific research can be shared between the poles on topics such as contaminant mitigation, biosecurity protocols to reduce species invasions, and the regulation of fisheries and marine environments. Applying these trans-polar lessons in tandem with expansion of international cooperation could substantially improve environmental management in both the Arctic and Antarctic.

Fisheries and biodiversity benefits of using static versus dynamic models for designing marine reserve networks

Marine reserves are widely used to manage for the potentially conflicting objectives of conserving biodiversity and improving fisheries. The fisheries and conservation benefits of alternative reserve designs would ideally be assessed using dynamic models, which consider spillover of fish and larvae to fished areas, and the displacement of fishers to unprotected sites. In practice, however, decisions about the location of marine reserves generally rely on cheaper and faster static models. Static models analyze only spatial patterns in habitats, and typically assume fisheries profits are reduced by the amount that was generated in areas designated as reserves. To help determine the benefits of developing dynamic fisheries models, we assessed how well static models estimate costs of reserve systems to fisheries and how outcomes from reserves designed using either static or dynamic models differ. We tested these questions in two case studies, the network of marine protected areas in southern California, USA and the proposed Tun Mustapha Marine Park in Malaysia. Static models could either under or over-estimate the cost of reserve plans to fisheries, depending on the relative importance of fisher movement and larval dispersal dynamics. Despite the inaccuracy of static models for estimating costs, reserves designed using static models were similar to those designed with dynamic models if fisheries were well managed; or larval dispersal networks were simple. If larval networks were complex or there was overfishing, dynamic models generated substantially different reserve networks from static models, which improved conservation outcomes by up to 10% and fishing profits by up 20%. The time-scale of management was also important, because only dynamic models accounted for larval dispersal, so could find reserves that maximized the long-term benefits of larval spillover. Our case studies provide quantitative support for the assertion that static models can be useful for planning marine reserves for short-term objectives in well managed fisheries, but are not reliable for evaluating the relative costs of reserves to fisheries.

Tracing the influence of land-use change on water quality and coral reefs using a Bayesian model

Coastal ecosystems can be degraded by poor water quality. Tracing the causes of poor water quality back to land-use change is necessary to target catchment management for coastal zone management. However, existing models for tracing the sources of pollution require extensive data-sets which are not available for many of the world’s coral reef regions that may have severe water quality issues. Here we develop a hierarchical Bayesian model that uses freely available satellite data to infer the connection between land-uses in catchments and water clarity in coastal oceans. We apply the model to estimate the influence of land-use change on water clarity in Fiji. We tested the model’s predictions against underwater surveys, finding that predictions of poor water quality are consistent with observations of high siltation and low coverage of sediment-sensitive coral genera. The model thus provides a means to link land-use change to declines in coastal water quality.

Opportunities and constraints for implementing integrated land–sea management on islands

Despite a growing body of literature on integrated land-sea management (ILSM), very little critical assessment has been conducted in order to evaluate ILSM in practice on island systems. Here we develop indicators for assessing 10 integrated island management principles and evaluate the performance of planning and implementation in four island ILSM projects from the tropical Pacific across different governance structures. We find that where customary governance is still strongly respected and enabled through national legislation, ILSM in practice can be very effective at restricting access and use according to fluctuations in resource availability. However, decision-making under customary governance systems may be vulnerable to mismanagement. Government-led ILSM processes have the potential to design management actions that address the spatial scale of ecosystem processes and threats within the context of national policy and legislation, but may not fully capture broad stakeholder interests, and implementation may be poorly coordinated across highly dispersed island archipelagos. Private sector partnerships offer unique opportunities for resourcing island ILSM, although these are highly likely to be geared towards private sector interests that may change in the future and no longer align with community and/or national objectives. We identify consistent challenges that arise during island ILSM planning and implementation and offer recommendations for improvement.

Simple rules can guide whether land- or ocean-based conservation will best benefit marine ecosystems

Coastal marine ecosystems can be managed by actions undertaken both on the land and in the ocean. Quantifying and comparing the costs and benefits of actions in both realms is therefore necessary for efficient management. Here, we quantify the link between terrestrial sediment runoff and a downstream coastal marine ecosystem and contrast the cost-effectiveness of marine- and land-based conservation actions. We use a dynamic land- and sea-scape model to determine whether limited funds should be directed to 1 of 4 alternative conservation actions—protection on land, protection in the ocean, restoration on land, or restoration in the ocean—to maximise the extent of light-dependent marine benthic habitats across decadal timescales. We apply the model to a case study for a seagrass meadow in Australia. We find that marine restoration is the most cost-effective action over decadal timescales in this system, based on a conservative estimate of the rate at which seagrass can expand into a new habitat. The optimal decision will vary in different social–ecological contexts, but some basic information can guide optimal investments to counteract land- and ocean-based stressors: (1) marine restoration should be prioritised if the rates of marine ecosystem decline and expansion are similar and low; (2) marine protection should take precedence if the rate of marine ecosystem decline is high or if the adjacent catchment is relatively intact and has a low rate of vegetation decline; (3) land-based actions are optimal when the ratio of marine ecosystem expansion to decline is greater than 1:1.4, with terrestrial restoration typically the most cost-effective action; and (4) land protection should be prioritised if the catchment is relatively intact but the rate of vegetation decline is high. These rules of thumb illustrate how cost-effective conservation outcomes for connected land–ocean systems can proceed without complex modelling.

A decision tree for assessing the risks and benefits of publishing biodiversity data

Inadequate information on the geographical distribution of biodiversity hampers decision-making for conservation. Major efforts are underway to fill knowledge gaps, but there are increasing concerns that publishing the locations of species is dangerous, particularly for species at risk of exploitation. While we recognize that well-informed control of location data for highly sensitive taxa is necessary to avoid risks, such as poaching or habitat disturbance by recreational visitors, we argue that ignoring the benefits of sharing biodiversity data could unnecessarily obstruct conservation efforts for species and locations with low risks of exploitation. We provide a decision tree protocol for scientists that systematically considers both the risks of exploitation and potential benefits of increased conservation activities. Our protocol helps scientists assess the impacts of publishing biodiversity data and aims to enhance conservation opportunities, promote community engagement and reduce duplication of survey efforts.Information on species abundances and distributions is essential for developing conservation policy and assessing change. Yet publically available data increases exploitation potential. This Perspective presents a decision framework to assess the risks and benefits of publically sharing biodiversity data.

Trade-offs between data resolution, accuracy, and cost when choosing information to plan reserves for coral reef ecosystems

Conservation planners must reconcile trade-offs associated with using biodiversity data of differing qualities to make decisions. Coarse habitat classifications are commonly used as surrogates to design marine reserve networks when fine-scale biodiversity data are incomplete or unavailable. Although finely-classified habitat maps provide more detail, they may have more misclassification errors, a common problem when remotely-sensed imagery is used. Despite these issues, planners rarely consider the effects of errors when choosing data for spatially explicit conservation prioritizations. Here we evaluate trade-offs between accuracy and resolution of hierarchical coral reef habitat data (geomorphology and benthic substrate) derived from remote sensing, in spatial planning for Kubulau District, Fiji. For both, we use accuracy information describing the probability that a mapped habitat classification is correct to design marine reserve networks that achieve habitat conservation targets, and demonstrate inadequacies of using habitat maps without accuracy data. We show that using more detailed habitat information ensures better representation of biogenic habitats (i.e. coral and seagrass), but leads to larger and more costly reserves, because these data have more misclassification errors, and are also more expensive to obtain. Reduced impacts on fishers are possible using coarsely-classified data, which are also more cost-effective for planning reserves if we account for data collection costs, but using these data may under-represent reef habitats that are important for fisheries and biodiversity, due to the maps low thematic resolution. Finally, we show that explicitly accounting for accuracy information in decisions maximizes the chance of successful conservation outcomes by reducing the risk of missing conservation representation targets, particularly when using finely classified data.