Mathematical Methods for Identifying Representative Reserve Networks

By Hugh P. Possingham, I. Ball, Sandy J. Andelman. 2000

778 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/77e4c907da114bf31e4803e7bd20329dcdb5ad19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. C. Klein
2. Atte Moilanen
3. R. Pressey
4. Joona Lehtomäki
5. Natalie Ban
6. N. J. Fox
7. L. Beckley
8. P. Armsworth

In Economics and Econometrics

1. Atte Moilanen
2. C. Klein
3. R. Pressey
4. Joona Lehtomäki
5. Natalie Ban
6. N. J. Fox
7. L. Beckley
8. P. Armsworth

In Management Science and Operations Research

1. C. Klein
2. Atte Moilanen
3. R. Pressey
4. Joona Lehtomäki
5. Natalie Ban
6. N. J. Fox
7. L. Beckley
8. P. Armsworth

Citations in the corpus

• USE OF POPULATION VIABILITY ANALYSIS AND RESERVE SELECTION ALGORITHMS IN REGIONAL CONSERVATION PLANS (2003, methodology): “Therefore, many current tools, such as the SITES model used here (Andelman et al. 1999, Possingham et al. 2000), employ heuristic algorithms to identify one or more ‘‘near-optimal’’ solutions that fulfill the selected goals efficiently.”
• An introduction to biodiversity concepts for environmental economists (2004, methodology): “In the MARXAN model, for example, which is employed in regional conservation planning, Possingham et al. (2000) use simulated annealing to minimize the following function: total cost = ∑ j Cj + ∑ i Pi − wb ∑ b, (6) where Cj is the cost of the jth site, Pi is a penalty for not meeting the…”
• Using habitat models to identify suitable sites for marine protected areas for harbour porpoises (Phocoena phocoena) (2010, methodology): “Optimal protected area design also requires consideration of its size, compactness and cost (Possingham et al., 2000; Leslie et al., 2003; Parnell et al., 2006).”
• Integrating species life‐history traits and patterns of deforestation in amphibian conservation planning (2010, methodology): “We used the software Marxan (v. 1.8.2) to evaluate and compare different conservation planning scenarios (Possingham et al., 2000).”
• Counterpart Models in Facility Location Science and Reserve Selection Science (2002, background): “Indeed, first attempts to model spatial attributes have also been made in reserve design, including: the designation of compact core areas and surrounding buffer zones [43,45]; the minimization of reserve perimeter [22, 25 ]; and the delineation of wildlife corridors [37,42].”
• Connectivity, Probabilities and Persistence: Comparing Reserve Selection Strategies (2006, background): “Several authors (Possingham et al. 2000; Nalle et al. 2002; Cabeza et al. 2004a, b) attempt to minimize negative external effects by aiming at the selection of aggregated reserves, by means of a penalty for boundary length.”
• Opportunity costs: Who really pays for conservation? (2010, methodology): “We used MARXAN software (Possingham et al., 2000) to design cost-effective reserves that met the conservation targets for all vegetation types.”
• Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis (2010, methodology): “However, modern machine learning techniques such as simulated annealing or genetic algorithms have successfully been used for similar purposes (Possingham et al. 2000; Guinand et al. 2002; Olden et al. 2008).”
• Forks in the Road: Choices in Procedures for Designing Wildland Linkages (2008, methodology): “Thus we did not consider simulated annealing approaches (Andelman et al. 1999; Possingham et al. 2000) or spatially explicit pop- ulation models (Carroll et al. 2003) that take a broader approach to reserve design, simultaneously prioritizing land both for core-habitat blocks and linkages between…”
• Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems (2005, background): “In summary, ecological, economic and logistic requirements demand that high connectivity be maintained for biodiversity priority areas (Hanski 1998; Debinski & Holt 2000; Gaston et al. 2002; Possingham et al. 2000).”
• Using Biogeography to Help Set Priorities in Marine Conservation (2004, background): “An aid in the second step, that of selecting conservation areas, is the growing number of mathematical reserveselection algorithms for marine applications (e.g., SITES and MARXAN; see www.ecology.uq.edu.au/marxan.htm) (Possingham et al. 2000).”
• The ecological effectiveness of protected areas: the United Kingdom (2006, background): “increasing risk of spread of diseases, invasive species; Possingham et al., 2000; Shafer, 2001).”
• PLUGGING A HOLE IN THE OCEAN: THE EMERGING SCIENCE OF MARINE RESERVES1 (2003, methodology): “The use of mathematical siting algorithms (Possingham et al. 2000, Airamé et al. 2003, Leslie et al. 2003), coupled with geographic information systems, provides an explicit and transparent mechanism for identifying spatially explicit maps of alternative reserve network scenarios that efficiently…”
• Informed opportunism for conservation planning in the Solomon Islands (2011, background): “There is a perception that the complexity and “black box” nature of spatial prioritization software can be an impediment to local engagement and decision making (Possingham et al. 2000).”
• Effectiveness of marine reserve networks in representing biodiversity and minimizing impact to fishermen: a comparison of two approaches used in California (2008, methodology): “Marxan uses a simulated annealing algorithm to select areas that minimize the sum of planning unit and boundary costs while ensuring that conservation targets are met (Possingham et al. 2000).”