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Biodiversity offsetting is a tool to balance ecological damage caused by human activity with new benefits created elsewhere. Offsetting is implemented by protecting, restoring or managing sufficiently large areas of habitat. While there are concerns about the true feasibility of offsetting, they are becoming a common policy tool world-wide. Operationally uncomplicated, quantitative approaches to spatial analysis of offsets are rare and their use is often restricted by the availability of suitable spatial data. We describe a practical method for offsets that builds upon two layers of relatively easily sourced spatial data, a balanced spatial priority ranking and a weighted range size rarity map. Together with (a) spatial information about impact and offset areas, and (b) extra parameters for the effectiveness of avoided loss and the amount of leakage expected, we can evaluate whether the proposed offset exchange represents a credible no net loss or net positive impact with an upward trade. The priority ranking and range size rarity maps can be produced in various ways, most notably using existing conservation planning tools. Here we used the standard outputs of the Zonation spatial prioritization software. We illustrate the method and associated visualization in the context of offsetting of boreal forests in Finland, where forests experience high and increasing pressures from forestry and bioenergy sectors. The example is timely as there is political demand for the uptake of biodiversity offset policies in Finland, and boreal forests are the most common biotope. The methods described here are applicable to biomes around the world. The described tools are made available as r scripts that utilize standard Zonation outputs, thus providing direct linkage to any past or future Zonation applications. As a limitation, the present methods only apply to avoided loss offsets. ; Peer reviewed

Policies and research increasingly focus on the protection of ecosystem services (ESs) through priority-area conservation. Priority areas for ESs should be identified based on ES capacity and ES demand and account for the connections between areas of ES capacity and demand (flow) resulting in areas of unique demand-supply connections (flow zones). We tested ways to account for ES demand and flow zones to identify priority areas in the European Union. We mapped the capacity and demand of a global (carbon sequestration), a regional (flood regulation), and 3 local ESs (air quality, pollination, and urban leisure). We used Zonation software to identify priority areas for ESs based on 6 tests: with and without accounting for ES demand and 4 tests that accounted for the effect of ES flow zone. There was only 37.1% overlap between the 25% of priority areas that encompassed the most ESs with and without accounting for ES demand. The level of ESs maintained in the priority areas increased from 23.2% to 57.9% after accounting for ES demand, especially for ESs with a small flow zone. Accounting for flow zone had a small effect on the location of priority areas and level of ESs maintained but resulted in fewer flow zones without ES maintained relative to ignoring flow zones. Accounting for demand and flow zones enhanced representation and distribution of ESs with local to regional flow zones without large trade-offs relative to the global ES. We found that ignoring ES demand led to the identification of priority areas in remote regions where benefits from ES capacity to society were small. Incorporating ESs in conservation planning should therefore always account for ES demand to identify an effective priority network for ESs.

Abstract Background One of the challenges facing the Global Polio Eradication Initiative is efficiently directing limited resources, such as specially trained personnel, community outreach activities, and satellite vaccinator tracking, to the most at-risk areas to maximize the impact of interventions. A validated predictive model of wild poliovirus circulation would greatly inform prioritization efforts by accurately forecasting areas at greatest risk, thus enabling the greatest effect of program interventions. Methods Using Nigerian acute flaccid paralysis surveillance data from 2004-2013, we developed a spatial hierarchical Poisson hurdle model fitted within a Bayesian framework to study historical polio caseload patterns and forecast future circulation of type 1 and 3 wild poliovirus within districts in Nigeria. A Bayesian temporal smoothing model was applied to address data sparsity underlying estimates of covariates at the district level. Results We find that calculated vaccine-derived population immunity is significantly negatively associated with the probability and number of wild poliovirus case(s) within a district. Recent case information is significantly positively associated with probability of a case, but not the number of cases. We used lagged indicators and coefficients from the fitted models to forecast reported cases in the subsequent six-month periods. Over the past three years, the average predictive ability is 86 ± 2% and 85 ± 4% for wild poliovirus type 1 and 3, respectively. Interestingly, the predictive accuracy of historical transmission patterns alone is equivalent (86 ± 2% and 84 ± 4% for type 1 and 3, respectively). We calculate uncertainty in risk ranking to inform assessments of changes in rank between time periods. Conclusions The model developed in this study successfully predicts districts at risk for future wild poliovirus cases in Nigeria. The highest predicted district risk was 12.8 WPV1 cases in 2006, while the lowest district risk was 0.001 WPV1 cases in 2013. Model results have been used to direct the allocation of many different interventions, including political and religious advocacy visits. This modeling approach could be applied to other vaccine preventable diseases for use in other control and elimination programs.

Angel sharks are among the most threatened species of sharks globally. Twenty-two species have been identified globally so far, with three species being present in the Mediterranean Sea: Squatina aculeata, Squatina oculata, and Squatina squatina. The Mediterranean populations of all three species have been assessed as Critically Endangered by the IUCN Red List of Threatened Species due to the steep decline of their populations as a result of their historical and current overexploitation by demersal fisheries. Therefore, currently there is an ongoing increasing effort for advancing the conservation of the species in the basin. Recently, in the context of the Regional Action Plan for Mediterranean Angel Sharks, the Aegean Sea and Crete have been identified as critical areas for all three species. This study provides the first predictive distribution map of the three angel shark species in the basin, while critical areas for the conservation of the species were identified through a systematic spatial conservation planning analysis. Our analysis revealed low overlapping between the existing MPA network and critical areas for the distribution of the species primarily in Greece and then Turkey, while 20% of the critical areas for the distribution of the species overlaps with Fisheries Restricted Areas of the region. This highlights the need for creating MPAs focusing on shark conservation within the Mediterranean that are currently completely absent. In addition, we provide policy recommendations that can secure better protection of angel sharks through the enforcement of the current legislations and the engagement of all relevant stakeholders.

During the past decade or two, spatial prioritization methods and software have been developed that can integrate large amounts of data in ecologically based spatial planning. Applications of these analyses include, for example, design of expansion of conservation area networks, ecological impact avoidance in infrastructure and other development projects, and land use zoning. The basic building block of these analyses is spatial data describing the distributions of many biodiversity features, including species and habitat types. Additional data about threats may be used to focus priorities either to areas that are threatened by anthoropogenic pressures, or to areas that presently are safely away from pressures. Data about costs and opportunity costs can be included to promote cost efficient solutions, which also are desirable from a societal perspective. Recently, there has been focus design of green (blue) infrasturcture and inclusion of ecosystem services into joint analyses together with biodiversity. It is a characteristic of ecosystem services that their connectivity requirements are more complicated than connectivity requirements of species or habitats. This is because accessibility of ecosystem seervices and their equitable availability to people generate connectivity requirements that are additional to the ecological connectivity requirements needed for the maintenance of ecosystem service provision. In this presentation I review the possibilities of large-scale ecologically based spatial prioritization and planning. Presently, our analytical capability will typically exceed the quality of the underlying data, meaning that data availability and quality are the factors that primarily limit the utility of analyses. Even so, data availability is fast improving, and well-informed ecological impact avoidance can be implemented in societal decision making if the political will to do so exists. Di Minin, E., Soutullo, A., Bartesaghi, L., Rios, M., Szephegyi, M. N. and A. Moilanen. 2017. Integrating biodiversity, ecosystem services and socio-economic data to identify priority areas and landowners for conservation actions at the national scale. Biological Conservation, 206: 56-64 Kukkala, A.S., and A. Moilanen 2016. Ecosystem services and connectivity in spatial conservation prioritization. Landscape ecology, 32: 5-14 Kareksela, S., Moilanen, A., Tuominen, S. and J.S. Kotiaho. 2013. Use of Inverse spatial conservation prioritization to avoid biodiversity loss outside protected areas. Conservation Biology, 27: 1294–1303. ; peerReviewed

There is high-level political support for the use of green infrastructure (GI) across Europe, to maintain viable populations and to provide ecosystem services (ES). Even though GI is inherently a spatial concept, the modern tools for spatial planning have not been recognized, such as in the recent European Environment Agency (EEA) report. We outline a toolbox of methods useful for GI design that explicitly accounts for biodiversity and ES. Data on species occurrence, habitats, and environmental variables are increasingly available via open-access internet platforms. Such data can be synthesized by statistical species distri- bution modeling, producing maps of biodiversity features. These, together with maps of ES, can form the basis for GI design. We argue that spatial conservation prioritization (SCP) methods are effective tools for GI design, as the overall SCP goal is cost-effective allocation of conservation efforts. Corridors are currently promoted by the EEA as the means for implementing GI design, but they typically target the needs of only a subset of the regional species pool. SCP methods would help to ensure that GI provides a balanced solution for the requirements of many biodiversity features (e.g., species, habitat types) and ES simultaneously in a cost- effective manner. Such tools are necessary to make GI into an operational concept for combating biodiversity loss and promoting ES. ; Peer reviewed

Pandemic influenza is an international public health concern. In light of the persistent threat of H5N1 avian influenza and the recent pandemic of A/H1N1swine influenza outbreak, public health agencies around the globe are continuously revising their preparedness plans. The A/H1N1 pandemic of 2009 demonstrated that influenza activity and severity might vary considerably among age groups and locations, and the distribution of an effective influenza vaccine may be significantly delayed and staggered. Thus, pandemic influenza vaccine distribution policies should be tailored to the demographic and spatial structures of communities. Here, we introduce a bi-criteria decision-making framework for vaccine distribution policies that is based on a geospatial and demographically-structured model of pandemic influenza transmission within and between counties of Arizona in the Unites States. Based on data from the 2009–2010 H1N1 pandemic, the policy predicted to reduce overall attack rate most effectively is prioritizing counties expected to experience the latest epidemic waves (a policy that may be politically untenable). However, when we consider reductions in both the attack rate and the waiting period for those seeking vaccines, the widely adopted pro rata policy (distributing according to population size) is also predicted to be an effective strategy.

Policies and research increasingly focus on the protection of ecosystem services (ESs) through priority-area conservation. Priority areas for ESs should be identified based on ES capacity and ES demand and account for the connections between areas of ES capacity and demand (flow) resulting in areas of unique demand-supply connections (flow zones). We tested ways to account for ES demand and flow zones to identify priority areas in the European Union. We mapped the capacity and demand of a global (carbon sequestration), a regional (flood regulation), and 3 local ESs (air quality, pollination, and urban leisure). We used Zonation software to identify priority areas for ESs based on 6 tests: with and without accounting for ES demand and 4 tests that accounted for the effect of ES flow zone. There was only 37.1% overlap between the 25% of priority areas that encompassed the most ESs with and without accounting for ES demand. The level of ESs maintained in the priority areas increased from 23.2% to 57.9% after accounting for ES demand, especially for ESs with a small flow zone. Accounting for flow zone had a small effect on the location of priority areas and level of ESs maintained but resulted in fewer flow zones without ES maintained relative to ignoring flow zones. Accounting for demand and flow zones enhanced representation and distribution of ESs with local to regional flow zones without large trade-offs relative to the global ES. We found that ignoring ES demand led to the identification of priority areas in remote regions where benefits from ES capacity to society were small. Incorporating ESs in conservation planning should therefore always account for ES demand to identify an effective priority network for ESs. ; Peer reviewed