Spatial Conservation Prioritization Research Articles

A review of regulation ecosystem services and disservices from faunal populations and potential impacts of agriculturalisation on their provision, globally

Land use and cover change (LUCC) is the main cause of natural ecosystem degradation and biodiversity loss and can cause a decrease in ecosystem service provision. Animal populations are providers of some key regulation services: pollination, pest and disease control and seed dispersal, the so-called faunal ecosystem services (FES). Here we aim to give an overview on the current and future status of regulation FES in response to change from original habitat to agricultural land globally. FES are much more tightly linked to wildlife populations and biodiversity than are most ecosystem services, whose determinants are largely climatic and related to vegetation structure. Degradation of ecosystems by land use change thus has much more potential to affect FES. In this scoping review, we summarise the main findings showing the importance of animal populations as FES providers and as a source of ecosystem disservices; underlying causes of agriculturalisation impacts on FES and the potential condition of FES under future LUCC in relation to the expected demand for FES globally. Overall, studies support a positive relationship between FES provision and animal species richness and abundance. Agriculturalisation has negative effects on FES providers due to landscape homogenisation, habitat fragmentation and loss, microclimatic changes and development of population imbalance, causing species and population losses of key fauna, reducing services whilst enhancing disservices. Since evidence suggests an increase in FES demand worldwide is required to support increased farming, it is imperative to improve the understanding of agriculturalisation on FES supply and distribution. Spatial conservation prioritisation must factor in faunal ecosystem functions as the most biodiversity-relevant of all ecosystem services and that which most closely links sites of service provision of conservation value with nearby sites of service use to provide ecosystem services of agricultural and economic value.

Not all data are equal: Influence of data type and amount in spatial conservation prioritisation

Abstract Decisions about land use significantly influence biodiversity globally. The field of spatial conservation prioritisation explores allocation of conservation effort, including for reserve network expansion, targeting habitat restoration, or minimising ecological impacts of development. Inevitably, the utility of such planning depends on the quantity and quality input data, including spatial information on biodiversity, threats, and cost of action. In this work we systematically develop understanding about the significance of these different data types in spatial conservation prioritisation. We clarify the common ways different data types enter an analysis, develop mathematical models to understand the effects of data in spatial prioritisation, and survey literature to establish typical quantities of different types of data used. We use Jackknife analysis to derive the expected change in site values, when a single new data layer is added to a prioritisation. We validate mathematical formulae for expected impacts using simulations. A survey of scientific literature reveals that typical spatial prioritisation analyses include hundreds of biodiversity feature layers (species, habitat types, ecosystem services), but the count of cost, threat or habitat condition layers is typically 0–5. Due to these differences, and the mathematical formulations commonly used to combine data types, the influence of a single cost, threat, or habitat condition data layer can be an order or two higher than the influence of a single biodiversity feature layer. In a classical cost‐effectiveness formulation (benefits divided by costs, B/C) the influence of a single cost layer can even be as large as the joint influence of thousands of species distributions. We also clarify how changes in data impact site values and spatial priority rankings differently, with the latter being further influenced by data correlations, the spread of numeric values inside data layers and other data characteristics. For example, costs influence priorities significantly if cost is positively correlated with biodiversity, but the correlation is the other way around for biodiversity and habitat condition. This work helps conservation practitioners to direct efforts when collating data for spatial conservation planning. It also helps decision makers understand where to focus attention when interpreting conservation plans and their uncertainties.

Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices

When development impacts a broad landscape and causes the loss of multiple ecosystem services, decisions about which of these impacts to offset must be made. We use industrial oil-palm developments in Kalimantan and quantify the potential for restoration to offset oil-palm impacts on carbon storage and biodiversity. We developed a unique backcasting approach combined with a spatial conservation prioritisation framework to identify priority areas for restoration offsetting. We calculated the past impacts of oil-palm development, quantified the future benefits of restoration for carbon storage and biodiversity over one oil-palm planting cycle of 25 years, and prioritised areas for restoration to balance the impacts and benefits for the least cost. We estimate that offsetting the carbon emissions attributable to the existing 4.6 Mha of industrial oil-palm plantation in Kalimantan is most cost-effectively achieved by restoring 0.4–1.6 Mha of degraded peatlands, including failed agricultural projects, at a cost of US$0.7–2.9 billion. On the other hand, offsetting biodiversity losses would require at least 4.7 Mha of degraded areas to be restored (equating to 8.7% of Kalimantan) at a cost of US$7.7 billion. We show that priority areas for offsetting biodiversity losses overlap poorly with those for compensating carbon emissions. Our analysis suggests that reconciling multiple impacts at landscape scales will necessitate difficult choices among contested socio-political preferences. Our findings also clarify the fundamental importance of conserving biodiversity-rich primary forests and peatlands in the tropics and the need to avoid converting these areas in the future.

Estimating land market values from real estate offers: A replicable method in support of biodiversity conservation strategies.

While cost estimation is a very positive tool for spatial conservation prioritisation, there are few examples where costs (in monetary terms) are applied. We present a repeatable method to estimate and map field values in monetary terms using common correlative models. We modelled, with a resolution of 1km2, the information obtained by several real estate's agencies with a set of eleven environmental, climatic, and anthropogenic variables. Land cover was the main influencing factor, but further variables were affecting bids on field sales according to the socio-economic specificity of each administrative province. The estimated values were related to endemic plant species richness, their conservation status and altitudinal ranges. Richest areas in endemics have lower values given current market conditions and, within these endemic rich areas, values near the coast were generally higher than the rest of endemic-rich territories. Despite their limits, our method offers an alternative perspective on the challenges of simplifying the extrapolation of useful information for planning and disseminating the conservation of many ecosystem services providers.

Influence of extent and data type on spatial conservation prioritization

Different extents, grains, and types of biodiversity data encompass the data sources used in investigations of spatial diversity. We evaluated the influence of geopolitical limits, data type, and grain size on complementarity networks of priority areas using Atlantic Forest primates, in Brazil. Priority areas were selected to represent primates from twelve research approaches that combine different data types (occurrence or distribution), geographical extents (entire biome or specific states) and grains (1°, 0.5° or 0.25° grids). We compared the size, irreplaceability and spatial concordance of the networks. Networks selected at the state extent had more grid cells than networks selected at the biome extent, independent of data type, and species locally rare but not globally rare may be driving these results. The number of irreplaceable cells and the networks selected were smaller using distribution data than using occurrence data. This effect was even more apparent at the state extent. Distribution data are more spatially distributed, leading to low irreplaceability of cells. Similarity among the selected networks depends on the extent and data type, and there was interaction between these factors. Similarities among networks selected at the state extent are higher than that of networks selected at biome extent. Networks obtained from different types of data were less similar than those selected using the same kind of data, and the ones selected using occurrence data were more similar than networks selected using distribution data. The effect of data type is stronger at smaller grain sizes, and the effect of extent is stronger at larger grain sizes. It is important to understand the consequences of data type choice, and this may be congruent with grain sizes.

Reserve design to optimize the long‐term persistence of multiple species

Protected areas form the cornerstone of global efforts to conserve biodiversity. Most current methods for designing reserve networks focus on maximizing the representation of species, but with no assurance that those species will persist in the protected landscapes into the future. We present a new strategy for reserve design that combines metapopulation theory with spatial conservation prioritization to estimate conservation solutions that minimize extinction risk across numerous species simultaneously. Our framework optimizes the spatial configuration of reserves to maximize metapopulation persistence for an entire assemblage of species by accounting for both species representation and landscape connectivity. As a case study, we design a reserve network for 114 terrestrial mammal species in Indonesian New Guinea. Our approach builds on Marxan, the flagship representation-based reserve design tool, improving estimated persistence (metapopulation capacity) by an average of 4.6-fold across species, without increasing the socioeconomic cost. We suggest that enhancing species persistence, rather than protecting arbitrary proportions of species' ranges, should be the ultimate objective of conservation planning.

Information uncertainty influences conservation outcomes when prioritizing multi‐action management efforts

AbstractIn managing various threats to biodiversity, it is important to prioritize multiple management actions and the levels of effort to apply. However, a spatial conservation prioritization framework that integrates these key aspects, and can be generalized, is still missing. Moreover, assessing the robustness of prioritization frameworks to uncertainty in species responses to management is critical to avoid misallocation of limited resources. Yet, the impact of information uncertainty on prioritization of management effort remains unknown.We present an approach for prioritizing alternative levels of conservation management effort to multiple actions, based on the ecological responses of species to management. We estimated species responses through a structured email‐based expert elicitation process, where we also captured the uncertainty in individual experts' assessments. We identified priority locations and associated level of management of effort of four actions to abate threats to freshwater‐dependent fauna, using a northern Australia case study, and quantified sensitivity of the proposed solution to uncertainty in the answers of each individual expert.Achievement of conservation targets for freshwater‐dependent fauna in the Daly River catchment would require 9.4 million AU$ per year, for a total of approximately 189 million AU$ investment over 20 years. We suggest that this could be best achieved through a mix of aerial shooting of buffalos and pigs, riparian fencing and chemical spraying of weeds, applied at varying levels of management effort in key areas of the catchment.Uncertainty in experts' estimation of species responses to threats causes 60% of the species to achieve 80% of their conservation targets, which was consistent across target levels.Synthesis and applications. Our prioritization approach facilitates the planning of conservation management at fine spatial scales and is applicable to terrestrial, freshwater and marine realms. Plan implementation may require policy instruments ranging from landowner stewardship agreements, market‐based mechanisms and low‐intensity land use management schemes, to regulation of commercial activities within portions of marine protected areas. However, assessing plan sensitivity to uncertainty in species response to management and finding ways of dealing with it in the prioritization rather than ignoring it, as often done, remains vital for effective achievement of conservation objectives.

Climate change likely to reduce orchid bee abundance even in climatic suitable sites.

Studies have tested whether model predictions based on species' occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence-absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence-absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability-abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest.

Salinity tolerances and use of saline environments by freshwater turtles: implications of sea level rise.

The projected rise in global mean sea levels places many freshwater turtle species at risk of saltwater intrusion into freshwater habitats. Freshwater turtles are disproportionately more threatened than other taxa; thus, understanding the role of salinity in determining their contemporary distribution and evolution should be a research priority. Freshwater turtles are a slowly evolving lineage; however, they can adapt physiologically or behaviourally to various levels of salinity and, therefore, temporarily occur in marine or brackish environments. Here, we provide the first comprehensive global review on freshwater turtle use and tolerance of brackish water ecosystems. We link together current knowledge of geographic occurrence, salinity tolerance, phylogenetic relationships, and physiological and behavioural mechanisms to generate a baseline understanding of the response of freshwater turtles to changing saline environments. We also review the potential origins of salinity tolerance in freshwater turtles. Finally, we integrate 2100 sea level rise (SLR) projections, species distribution maps, literature gathered on brackish water use, and a phylogeny to predict the exposure of freshwater turtles to projected SLR globally. From our synthesis of published literature and available data, we build a framework for spatial and phylogenetic conservation prioritization of coastal freshwater turtles. Based on our literature review, 70 species (∼30% of coastal freshwater turtle species) from 10 of the 11 freshwater turtle families have been reported in brackish water ecosystems. Most anecdotal records, observations, and descriptions do not imply long-term salinity tolerance among freshwater turtles. Rather, experiments show that some species exhibit potential for adaptation and plasticity in physiological, behavioural, and life-history traits that enable them to endure varying periods (e.g. days or months) and levels of saltwater exposure. Species that specialize on brackish water habitats are likely to be vulnerable to SLR because of their exclusive coastal distributions and adaptations to a narrow range of salinities. Most species, however, have not been documented in brackish water habitats but may also be highly vulnerable to projected SLR. Our analysis suggests that approximately 90% of coastal freshwater turtle species assessed in our study will be affected by a 1-m increase in global mean SLR by 2100. Most at risk are freshwater turtles found in New Guinea, Southeast Asia, Australia, and North and South America that may lose more than 10% of their present geographic range. In addition, turtle species in the families Chelidae, Emydidae, and Trionychidae may experience the greatest exposure to projected SLR in their present geographic ranges. Better understanding of survival, growth, reproductive and population-level responses to SLR will improve region-specific population viability predictions of freshwater turtles that are increasingly exposed to SLR. Integrating phylogenetic, physiological, and spatial frameworks to assess the effects of projected SLR may improve identification of vulnerable species, guilds, and geographic regions in need of conservation prioritization. We conclude that the use of brackish and marine environments by freshwater turtles provides clues about the evolutionary processes that have prolonged their existence, shaped their unique coastal distributions, and may prove useful in predicting their response to a changing world.

Spatial alternatives for Green Infrastructure planning across the EU: An ecosystem service perspective

Target 2 of the EU Biodiversity Strategy to 2020 aims at the deployment of Green Infrastructure (GI) and the restoration of at least 15% of degraded ecosystems. We assess different alternatives for the spatial planning of GI and ecosystem restoration across the European Union by using spatial conservation prioritization tools. We compared three different scenarios for the identification of priority areas in which the ecosystem service potential, beneficiaries (i.e. people) and ecosystem condition play different roles. As an example of GI restoration, we also assessed the cost-effectiveness of removal of invasive alien species in the areas prioritized under each scenario.The comparative assessment of the spatial alternatives for GI shows synergies and conflicts. We found that GI could be efficiently established close to densely populated areas, since high multi-functionality is delivered in these locations (close to human settlements). However, restoration costs, such as the removal of invasive alien species, were higher in such areas given the influence of urban pressures. We also found that GI prioritized in areas under poor ecosystem condition would require a larger spatial extent of implementation, due to a lower ecosystem service potential per unit area.Given the scarcity of resources for investment in GI and ecosystem restoration, win-win situations should be identified where GI designation can deliver several policy objectives simultaneously. The prioritization framework we have presented here could also be applied at the country or regional level to support local planning.

Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus).

The purpose of this study was to determine whether limited occurrence data for highly threatened species can provide useful spatial information to inform conservation. The study was conducted across central and southern China. We developed a habitat suitability model for the Critically Endangered Chinese giant salamander (Andrias davidianus) based on one biotic and three abiotic parameters from single‐site locality records, which represent the only relevant environmental data available for this species. We then validated model quality by testing whether increased percentage of predicted suitable habitat at the county level correlated with independent data on giant salamander presence. We randomly selected 48 counties containing historical records which were distinct from, and independent of, the single‐site records used to develop the model, and 47 additional counties containing >50% predicted suitable habitat. We interviewed 2,812 respondents near potential giant salamander habitat across these counties and tested for differences in respondent giant salamander reports between counties selected using each method. Our model predicts that suitable giant salamander habitat is found widely across central and southern China, with counties containing ≥50% predicted suitable habitat distributed in 13 provinces. Counties with historical records contain significantly more predicted suitable habitat than counties without historical records. There are no statistical differences in any patterns of respondent giant salamander reports in surveyed counties selected from our model compared with the areas of known historical giant salamander distribution. A Chinese giant salamander habitat suitability model with strong predictive power can be derived from the restricted range of environmental variables associated with limited available presence‐only occurrence records, constituting a cost‐effective strategy to guide spatial allocation of conservation planning. Few reported sightings were recent, however, with most being over 20 years old, so that identification of areas of suitable habitat does not necessarily indicate continued survival of the species at these locations.

Integrating airborne lidar and satellite imagery to model habitat connectivity dynamics for spatial conservation prioritization

The application of regional-level airborne lidar (light detection and ranging) data to characterize habitat patches and model habitat connectivity over large landscapes has not been well explored. Maintaining a connected network of habitat in the presence of anthropogenic disturbances is essential for regional-level conservation planning and the maintenance of biodiversity values. We quantified variation in connectivity following simulated changes in land cover and contrasted outcomes when different conservation priorities were emphasized. First, we defined habitat patches using vegetation structural attributes identified via lidar. Second, habitat networks were constructed for different forest types and assessed using network connectivity metrics. And finally, land cover change scenarios were simulated using a series of habitat patch removals, representing the impact of implementing different spatial prioritization schemes. Networks for different forest structure types produced very different patch distributions. Conservation scenarios based on different schemes led to contrasting changes during land cover change simulations: the scheme prioritizing only habitat area resulted in immediate near-term losses in connectivity, whereas the scheme considering both habitat area and their spatial configurations maintained the overall connectivity most effectively. Adding climate constraints did not diminish or improve overall connectivity. Both habitat area and habitat configuration should be considered in dynamic modeling of habitat connectivity under changing landscapes. This research provides a framework for integrating forest structure and cover attributes obtained from remote sensing data into network connectivity modeling, and may serve as a prototype for multi-criteria forest management and conservation planning.

Exposing ecological and economic costs of the research-implementation gap and compromises in decision making.

The frequently discussed gap between conservation science and practice is manifest in the gap between spatial conservation prioritization plans and their implementation. We analyzed the research-implementation gap of one zoning case by comparing results of a spatial prioritization analysis aimed at avoiding ecological impact of peat mining in a regional zoning process with the final zoning plan. We examined the relatively complex planning process to determine the gaps among research, zoning, and decision making. We quantified the ecological costs of the differing trade-offs between ecological and socioeconomic factors included in the different zoning suggestions by comparing the landscape-level loss of ecological features (species occurrences, habitat area, etc.) between the different solutions for spatial allocation of peat mining. We also discussed with the scientists and planners the reasons for differing zoning suggestions. The implemented plan differed from the scientists suggestion in that its focus was individual ecological features rather than all the ecological features for which there were data; planners and decision makers considered effects of peat mining on areas not included in the prioritization analysis; zoning was not truly seen as a resource-allocation process and not emphasized in general minimizing ecological losses while satisfying economic needs (peat-mining potential); and decision makers based their prioritization of sites on site-level information showing high ecological value and on single legislative factors instead of finding a cost-effective landscape-level solution. We believe that if the zoning and decision-making processes are very complex, then the usefulness of science-based prioritization tools is likely to be reduced. Nevertheless, we found that high-end tools were useful in clearly exposing trade-offs between conservation and resource utilization.

Threats from urban expansion, agricultural transformation and forest loss on global conservation priority areas.

Including threats in spatial conservation prioritization helps identify areas for conservation actions where biodiversity is at imminent risk of extinction. At the global level, an important limitation when identifying spatial priorities for conservation actions is the lack of information on the spatial distribution of threats. Here, we identify spatial conservation priorities under three prominent threats to biodiversity (residential and commercial development, agricultural expansion, and forest loss), which are primary drivers of habitat loss and threaten the persistence of the highest number of species in the International Union for the Conservation of Nature (IUCN) Red List, and for which spatial data is available. We first explore how global priority areas for the conservation of vertebrate (mammals, birds, and amphibians) species coded in the Red List as vulnerable to each threat differ spatially. We then identify spatial conservation priorities for all species vulnerable to all threats. Finally, we identify the potentially most threatened areas by overlapping the identified priority areas for conservation with maps for each threat. We repeat the same with four other well-known global conservation priority area schemes, namely Key Biodiversity Areas, Biodiversity Hotspots, the global Protected Area Network, and Wilderness Areas. We find that residential and commercial development directly threatens only about 4% of the global top 17% priority areas for species vulnerable under this threat. However, 50% of the high priority areas for species vulnerable to forest loss overlap with areas that have already experienced some forest loss. Agricultural expansion overlapped with ~20% of high priority areas. Biodiversity Hotspots had the greatest proportion of their total area under direct threat from all threats, while expansion of low intensity agriculture was found to pose an imminent threat to Wilderness Areas under future agricultural expansion. Our results identify areas where limited resources should be allocated to mitigate risks to vertebrate species from habitat loss.

Spatial conservation of water yield and sediment retention hydrological ecosystem services across Teshio watershed, northernmost of Japan

There is a growing call for spatial conservation prioritization of ecosystem services (ESs) models that is both simple and scientifically credible, in order to serve ecological and environmental decision-making processes. Pressure on ecosystems to provide various and conflicting services is immense and likely to increase. Despite increasing attention to the human dimension of conservation projects, a rigorously spatial conservation planning on balancing multiple ESs has not been developed. The impacts and success of spatial conservation planning will be enhanced if the needs of competing and compromising ESs are recognized. We developed such a framework integrating watershed model into spatial conservation prioritization model and illustrated it about competing and compromising ESs in the Teshio River watershed, with the aim of developing a spatial conservation priority ranking map that balances interactive relationships between water yield and sediment retention. The sediment retention was concentrated in southeastern and some northern areas with higher precipitation, more forest lands and steeper slope, but the water yield was concentrated in southwestern and some northern places with agricultural land. The spatial priority conservation ranking map of individual ES is closely related to its spatially distributed pattern. The spatial priority conservation areas for sediment retention in southwest are traded off against those for water yield in southeast, but there are some overlaps on spatial priority conservation areas for sediment retention and water yield in north of Teshio River watershed. There are obvious differences between the spatial priority conservation ranking maps of individual ESs and those of multiple services together. The spatial priority conservation areas for multiple ESs together simultaneously include southeastern, more eastern and some northern places of study watershed, which can balance the conflict existing between sediment retention and water yield. The proposed framework in this study could be applied to similarly structural conservation prioritization problems of other more ESs, which could sustain ecosystem conservation and economic development across watershed.

3D spatial conservation prioritisation: Accounting for depth in marine environments

Abstract While marine environments are three‐dimensional (3D) in nature, current approaches and tools for planning and prioritising actions in the ocean are predominantly two dimensional. Here, we develop a novel 3D marine spatial conservation prioritisation approach, which explicitly accounts for the inherent vertical heterogeneity of the ocean. This enables both vertical and horizontal spatial prioritisation to be performed simultaneously. To our knowledge, this is the first endeavour to develop prioritisation of conservation actions in 3D. We applied the 3D spatial conservation prioritisation approach to the Mediterranean Sea as a case study. We first subdivided the Mediterranean Sea into 3D planning units by assigning them a z coordinate (representing depth). We further partitioned these 3D planning units vertically into three depth layers; this allowed us to quantify biodiversity (1,011 species and 19 geomorphic features) and the cost of conservation actions at different depths. We adapted the prioritisation software Marxan to identify 3D networks of sites where biodiversity conservation targets are achieved for the minimum cost. Using the 3D approach presented here, we identified networks of sites where conservation targets for all biodiversity features were achieved. Importantly, these networks included areas of the ocean where only particular depth layers along the water column were identified as priorities for conservation. The 3D approach also proved to be more cost‐efficient than the traditional 2D approach. Spatial priorities within the networks of sites selected were considerably different when comparing the 2D and 3D approaches. Prioritising in 3D allows conservation and marine spatial planners to target specific threats to specific conservation features, at specific depths in the ocean. This provides a platform to further integrate systematic conservation planning into the wider ongoing and future marine spatial planning and ocean zoning processes.

Preference heterogeneity among landowners and spatial conservation prioritization: response to Eyvindson etal. 2017.

Preference heterogeneity among landowners and spatial conservation prioritization: response to Eyvindson etal. 2017.

Differences in spatial conservation prioritization: A multicriteria analysis in the Neotropics

Differences in spatial conservation prioritization: A multicriteria analysis in the Neotropics

An Evaluation of Marine Important Bird and Biodiversity Areas in the Context of Spatial Conservation Prioritization

AbstractImportant Bird and Biodiversity Areas (IBAs) are sites identified as globally important for bird species conservation. Marine IBAs are one of the few comprehensive multi‐species datasets available for the marine environment, and their use in conservation planning will likely increase as countries race to protect 10% of their territorial waters by 2020. We tested 15 planning scenarios for Australia's Exclusive Economic Zone to guide best practice on integrating marine IBAs into spatial conservation prioritization. We found prioritizations based solely on habitat protection failed to protect IBAs, and prioritizations based solely on IBAs similarly failed to meet basic levels of habitat representation. Further, treating all marine IBAs as irreplaceable sites produced the most inefficient plans in terms of ecological representativeness and protection equality. Our analyses suggest that marine spatial planners who wish to use IBAs treat them like any other conservation feature by assigning them a specific protection target.

Designing protected area networks that translate international conservation commitments into national action

Most countries have committed to protect 17% of their terrestrial area by 2020 through Aichi Target 11 of the Convention on Biological Diversity, with a focus on protecting areas of particular importance for biodiversity. This means national-scale spatial conservation prioritisations are needed to help meet this target and guide broader conservation and land-use policy development. However, to ensure these assessments are adopted by policy makers, they must also consider national priorities. This situation is exemplified by Guyana, a corner of Amazonia that couples high biodiversity with low economic development. In recent years activities that threaten biodiversity conservation have increased, and consequently, protected areas are evermore critical to achieving the Aichi targets. Here we undertake a cost-effective approach to protected area planning in Guyana that accounts for in-country conditions. To do this we conducted a stakeholder-led spatial conservation prioritisation based on meeting targets for 17 vegetation types and 329 vertebrate species, while minimising opportunity costs for forestry, mining, agriculture and urbanisation. Our analysis identifies 3millionha of priority areas for conservation, helping inform government plans to double the current protected area network from 8.5 to 17%. As part of this, we also develop a new technique to prioritise engagement with local communities whose lands are identified as important to conservation. Our study both provides a scientifically robust, politically acceptable protected area expansion strategy for Guyana, and illustrates the importance of conservation planning at the country-scale to translate international commitments into national action.