Managing risk and uncertainty in systematic conservation planning with insufficient information

Cross taxa congruence was investigated between butterfly taxa and ecological community for fine spatial scale (10 × 10 km² UTM grids) in north-eastern part of Turkey. The study area was evaluated within the scope of systematic conservation planning, and analyses were performed for sets of priority protected areas composed using complementarity-based site selection software Marxan. Cross taxa congruence was subsequently examined both in species richness and ecologic complementarity. Accordingly, it has been observed that the cross-taxon congruence between butterfly taxa and ecological community was relatively better than the results of previous studies. Another remarkable finding is that ecological community was a more robust surrogate than butterfly taxa. Although the results are valuable for conservation studies, they highlight the fact that a simple surrogate-based site selection would be inadequate to represent overall biodiversity. The weakness of congruence patterns among surrogates would also lead to gaps in biodiversity conservation. These findings therefore draw attention to the necessities of incorporating surrogates of distinct ecology or some other surrogates like environmental parameters into conservation planning. Otherwise, there may be mistakes regarding species representation and the vast majority of species may be misrepresented in protected areas and protected area plans. At this point, it should be emphasized that understating cross taxa congruence and/or relationships is a key component for efficient biodiversity conservation.

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Are trade-offs between flexibility and efficiency in systematic conservation planning avoidable ?

10.1101/775072 ◽

2019 ◽

Author(s):

Sabrine Drira ◽

Frida Ben Rais Lasram ◽

Tarek Hattab ◽

Yunne Jai Shin ◽

Amel Ben Rejeb Jenhani ◽

...

Keyword(s):

Conservation Planning ◽

Large Body ◽

Efficient Solutions ◽

Reserve Selection ◽

Systematic Conservation Planning ◽

Distribution Models ◽

Precise Control ◽

Trade Offs ◽

Selection Framework ◽

Selection Of

AbstractSpecies distribution models (SDMs) have been proposed as a way to provide robust inference about species-specific sites suitabilities, and have been increasingly used in systematic conservation planning (SCP) applications. However, despite the fact that the use of SDMs in SCP may raise some potential issues, conservation studies have overlooked to assess the implications of SDMs uncertainties. The integration of these uncertainties in conservation solutions requires the development of a reserve-selection approach based on a suitable optimization algorithm. A large body of research has shown that exact optimization algorithms give very precise control over the gap to optimality of conservation solutions. However, their major shortcoming is that they generate a single binary and indivisible solution. Therefore, they provide no flexibility in the implementation of conservation solutions by stakeholders. On the other hand, heuristic decision-support systems provide large amounts of sub-optimal solutions, and therefore more flexibility. This flexibility arises from the availability of many alternative and sub-optimal conservation solutions. The two principles of efficiency and flexibility are implicitly linked in conservation applications, with the most mathematically efficient solutions being inflexible and the flexible solutions provided by heuristics suffering sub-optimality. In order to avoid the trade-offs between flexibility and efficiency in systematic conservation planning, we propose in this paper a new reserve-selection framework based on mathematical programming optimization combined with a post-selection of SDM outputs. This approach leads to a reserve-selection framework that might provide flexibility while simultaneously addressing efficiency and representativeness of conservation solutions and the adequacy of conservation targets. To exemplify the approach we a nalyzed an experimental design crossing pre- and post-selection of SDM outputs versus heuristics and exact mathematical optimizations. We used the Mediterranean Sea as a biogeographical template for our analyses, integrating the outputs of 8 SDM techniques for 438 fishes species.

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Systematic Conservation Planning: Past, Present and Future

Conservation Biogeography ◽

10.1002/9781444390001.ch6 ◽

2011 ◽

pp. 136-160 ◽

Cited By ~ 33

Author(s):

James E. M. Watson ◽

Hedley S. Grantham ◽

Kerrie A. Wilson ◽

Hugh P. Possingham

Keyword(s):

Conservation Planning ◽

Systematic Conservation Planning

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Complexity Theory and System Dynamics for Project Risk Management

Advances in IT Personnel and Project Management - Handbook of Research on Leveraging Risk and Uncertainties for Effective Project Management ◽

10.4018/978-1-5225-1790-0.ch008 ◽

2017 ◽

pp. 152-176 ◽

Cited By ~ 1

Author(s):

Robert J. Chapman

Keyword(s):

System Dynamics ◽

Systems Thinking ◽

Complexity Theory ◽

Systems Dynamics ◽

Risk And Uncertainty ◽

Causal Loop ◽

General Systems ◽

Big Picture ◽

Causal Loop Diagrams ◽

Managing Risk

As a consequence of the consensus that projects are growing in complexity from ever ambitious goals there is a perpetual search for methods aimed at pinpointing and describing the source of complexity with the objective of subsequently reducing uncertainty, managing risk and improving project performance. An area of study that has engaged enquiring minds for over fifty years but has not yet been accepted into mainstream project management is the study and application of systems thinking and system dynamics. The purpose of this chapter is to promulgate the view that the mapping of projects as systems should be re-examined as a means of articulating and responding to complexity. The chapter examines general systems theory, systems thinking and systems dynamics with examples of causal loop diagrams as an aid to describe and respond to risk exposure. It includes simple causal loop diagrams as a means of illustrating how risks may be identified and addressed. The emphasis is on seeing the ‘big picture' to avoid gaps and omissions in the management of risk and uncertainty.

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