Methods and workflow for spatial conservation prioritization using Zonation

Spatial conservation prioritization concerns the effective allocation of conservation action. Its stages include development of an ecologically based model of conservation value, data pre-processing, spatial prioritization analysis, and interpretation of results for conservation action. Here we investigate the details of each stage for analyses done using the Zonation prioritization framework. While there is much literature about analytical methods implemented in Zonation, there is only scattered information available about what happens before and after the computational analysis. Here we fill this information gap by summarizing the pre-analysis and post-analysis stages of the Zonation framework. Concerning the entire process, we summarize the full workflow and list examples of operational best-case, worst- case, and typical scenarios for each analysis stage. We discuss resources needed in different analysis stages. We also discuss benefits, disadvantages, and risks involved in the application of spatial prioriti- zation from the perspective of different stakeholders. Concerning pre-analysis stages, we explain the development of the ecological model and discuss the setting of priority weights and connectivity re- sponses. We also explain practical aspects of data pre-processing and the post-processing interpretation of results for different conservation objectives. This work facilitates well-informed design and application of Zonation analyses for the purpose of spatial conservation planning. It should be useful for both sci- entists working on conservation related research as well as for practitioners looking for useful tools for conservation resource allocationPeer reviewe

How do recent spatial biodiversity analyses support the convention on biological diversity in the expansion of the global conservation area network?

ABSTRACTIn the tenth Conference of Parties to the Convention on Biological Diversity (CBD) held in Nagoya in 2010, it was decided that 17% of terrestrial and 10% of marine areas should be protected globally by 2020. It was also stated that conservation decision-making should be based on sound science. Here, we review how recent scientific literature about spatial conservation prioritization analyses and macro-ecology corresponds to the information needs posed by the Aichi Biodiversity Target 11. A literature search was performed in Web of Science to identify potentially relevant research articles published in 2010-2012. Additionally, we searched all articles published since 2000 in five high-profile scientific journals. The studies were classified by extent and resolution, and we evaluated the type and breadth of data that was utilized (This information is included in a supplementary table to facilitate further research). Implementation of the Aichi Targets would best be supported by broad-extent, high-resolution, and data-rich studies that can directly support realistic decision-making about allocation of conservation efforts at sub-continental to global extents. When looking at all evaluation criteria simultaneously, we found little research that directly supports the analytical needs of the CBD. There are many narrow- extent, low-resolution, narrow-scope, or theoretically-aimed studies that are important in developing theory and local practices, but which are not adequate for guiding conservation management at a continental scale. Even national analyses are missing for many countries. Global-extent, high-resolution analyses using broad biodiversity and anthropogenic data are needed in order to inform decision making under the CBD resolutions.© 2014 Associação Brasileira de Ciência Ecológica e Conservação. Published by Elsevier Editora Ltda

Identification of top priority areas and management landscapes from a national Natura 2000 network

AbstractThe Natura 2000 (N2k) network of protected areas is a backbone of biodiversity conservation in Europe, with likely further relevance for the development of green infrastructure. EU member states have legal responsibilities for evaluating the condition of and maintaining their national networks. While it is desirable to maintain the condition of the N2k network or even improve it by habitat restoration, it is a fact that national environmental bodies operate under budgetary constraints – money available for conservation is limited. Consequently, there may be a need to prioritize targeting of conservation effort in and around the N2k network. In this study we develop a high-resolution spatial conservation prioritization for the Finnish national N2k network, using data about the distribution and quality of 68 N2k habitats occurring in Finland. The aim of the work is to identify management landscapes, landscapes that have exceptionally high conservation value and which could be managed as one management unit. We identify top-priority areas of the N2k network. We also identify highest-priority N2k areas that do not have the status of a protected area in the Finnish legislation. The present work was commissioned by the Natural Heritage Services of Metsähallitus, a national administrator that is responsible for the maintenance of the Finnish protected area network. The primary purpose of this work is to assist targeting of habitat maintenance, management and restoration in and around the Finnish N2k network. The analysis done here could be replicated elsewhere using publicly available spatial prioritization software

New performance guarantees for the greedy maximization of submodular set functions

We present new tight performance guarantees for the greedy maximization of monotone submodular set functions. Our main result first provides a performance guarantee in terms of the overlap of the optimal and greedy solutions. As a consequence we improve performance guarantees of Nemhauser et al. (Math Program 14: 265-294, 1978) and Conforti and Cornuejols (Discr Appl Math 7: 251-274, 1984) for maximization over subsets, which are at least half the size of the problem domain. As a further application, we obtain a new tight approximation guarantee in terms of the cardinality of the problem domain.Peer reviewe

FORUM : Indirect leakage leads to a failure of avoided loss biodiversity offsetting

Biodiversity offsetting has quickly gained political support all around the world. Avoided loss (averted risk) offsetting means compensation for ecological damage via averted loss of anticipated impacts through the removal of threatening processes in compensation areas. Leakage means the phenomenon of environmentally damaging activity relocating elsewhere after being stopped locally by avoided loss offsetting. Indirect leakage means that locally avoided losses displace to other administrative areas or spread around diffusely via market effects. Synthesis and applications. Indirect leakage can lead to high net biodiversity loss. It is difficult to measure or prevent, raising doubts about the value of avoided loss offsetting. Market demand for commodities is on the rise, following increasing human population size and per capita consumption, implying that indirect leakage will be a rule rather than an exception. Leakage should be accounted for when determining offset multipliers (ratios) even if multipliers become extremely high.Peer reviewe

Ecosystem services and connectivity in spatial conservation prioritization

Peer reviewe

Suojelualueverkoston suunnittelu matemaattisena ongelmana

Luonnonsuojelun tärkein päämäärä on turvata biologisen monimuotoisuuden säilyminen pitkälle tulevaisuuteen. Ihmisen toiminnasta johtuvaa luonnollisten elinympäristöjen häviämistä ja pirstoutumista pidetään yleisesti suurimpana maailmanlaajuisena uhkana eliölajien säilymiselle (Pimm & Lawton 1998). Kotimaisia esimerkkejä elinympäristöjen häviämisestä ovat vanhojen metsien ja ketomaisemien määrän huomattava väheneminen 1900-luvun loppupuoliskolla. Pirstoutumisella on merkittäviä vaikutuksia myös suojelualueverkoston suunnitteluun

Where and how to manage: Optimal selection of conservation actions for multiple species.

Multiple alternative options are frequently available for the protection, maintenance or restoration of conservation areas. The choice of a particular management action can have large effects on the species occurring in the area, because different actions have different effects on different species. Together with the fact that conservation funds are limited and particular management actions are costly, it would be desirable to be able to identify where, and what kind of management should be applied to maximize conservation benefits. Currently available site-selection algorithms can identify the optimal set of sites for a reserve network. However, these algorithms have not been designed to answer what kind of action would be most beneficial at these sites when multiple alternative actions are available. We describe an algorithm capable of solving multi-species planning problems with multiple management options per site. The algorithm is based on benefit functions, which translate the effect of a management action on species representation levels into a value, in order to identify the most beneficial option. We test the performance of this algorithm with simulated data for different types of benefit functions and show that the algorithm’s solutions are optimal, or very near globally optimal, partially depending on the type of benefit function used. The good performance of the proposed algorithm suggests that it could be profitably used for large multi-action multi-species conservation planning problems

Reserve Selection Using Nonlinear Species Distribution Models

Reserve design is concerned with optimal selection of sites for new conservation areas. Spatial reserve design explicitly considers the spatial pattern of the proposed reserve network and the effects of that pattern on reserve cost and/or ability to maintain species there. The vast majority of reserve selection formulations have assumed a linear problem structure, which effectively means that the biological value of a potential reserve site does not depend on the pattern of selected cells. However, spatial population dynamics and autocorrelation cause the biological values of neighboring sites to be interdependent. Habitat degradation may have indirect negative effects on biodiversity in areas neighboring the degraded site as a result of, for example, negative edge effects or lower permeability for animal movement. In this study, I present a formulation and a spatial optimization algorithm for nonlinear reserve selection problems in gridbased landscapes that accounts for interdependent site values. The method is demonstrated using habitat maps and nonlinear habitat models for threatened birds in the Netherlands, and it is shown that near-optimal solutions are found for regions consisting of up to hundreds of thousands grid cells, a landscape size much larger than those commonly attempted even with linear reserve selection formulations