At the same time that thousands of species are threatened to extinction, economic globalization diverts investments from long-term projects to fast renting policies, putting the majority of governmental and non-governmental conservation organizations under severe budget shortage. Under this ambiance of crisis conservation planners are commonly encouraged to use their budgets for the protection of single charismatic species or single species assumed highly threatened and/or that present very peculiar conservation requirements (e.g. many LIFE+ projects taking place in the European Union: Conservation of the Iberian lynx and Black Vulture; Conservation of Bonelli’s Eagle and several projects taking place in Americas, Africa, Asia and Oceania for the protection of habitat for iconic species). Although decisive in many circumstances, single species planning designs (even the ones supported by quantitative tools that foster cost-effectiveness) cannot be generalized. Single-species plans are commonly tailored under the idea of the maximum benefit for the targeted species with no explicit concerns and guidance for the great majority of local/ regional biodiversity. These strategies are myopic by nature given that they do not integrate the possible (positive or negative) impacts of conservation actions over the species that interact with the targeted species or that share the same habitats. But curiously, given that pocket nature of many threats it is likely that many species, distributing in the same areas are exposed to the same threats and therefore might also be facing important stresses, possibly sharing the same conservation requirements.

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The very first step to rationalize the use of the limited budgets available for biodiversity conservation is therefore to merge several species at risk into conservation plans for multiple species. Under these strategies the financial resources that serve to carry out a conservation action for a given species might be used in parallel to protect other species with the same ecological needs. For example, in area prioritization plans the sole protection of a given area may favor (at least partially) the conservation of all the species occupying that area and, synergistically, many of the species depending on them. In multiple-species planning frameworks the distribution of financial resources implies that trade-offs among species are tacitly made, making the control of conservation effectiveness for each of the species a challenge.

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In order to take full-control on results planners frequently define minimum viable levels to represent each species within conservation areas (aka conservation targets). A species is considered adequately protected if the areas to prioritize allow a given distribution area, amount of individuals or amount of suitable habitat for a species to be safeguarded. In the last decades several target-based spatial conservation models have been proposed (cover problem, expected number of species, connectivity, or climate change).

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The majority of these models rely on two basic problem types in which the benefits and costs related to a decision (e.g. conservation area selection) are tuned differently. In the minimum cost cover problem requests that in final solutions the established targets for species are fulfilled using the less financial resources. Given the high constraint nature of the problem, feasibility to obtain solution is dependent on the fulfillment of all targets. If only one target is missed by a small amount, a problem gets infeasible. Under such circumstances, planners might consider to run the analyses dismissing the faulty species. Other possibility is to run iteratively the problem by lowering the target a species a time. Given the number of species this might not be operationally realized and does not suffice to optimize the costs.

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In a related problem (the maximal coverage problem) a fixed budget is defined at hand and area prioritization is made such that the number of species with a representation over a specified target is maximized. This is the most likely problem faced by conservation planners. In this problem the role of conservation targets is to define a representation threshold over which a species is considered covered. In the original form of both problem types the benefits associated to investments made under sub-target circumstances are not integrated in the area prioritization procedure. That is, areas are selected such that in the end a solution is only dependent on the achievement or failing of species targets. If, whatever the investment made on a species its target is not satisfied, then the species is assumed condemned and taken from the conservation plan. The maximum possible representation of those species in a final solution set is not accounted even if they enable the species target to be missed by a small amount. When there is the need to consolidate several species and/or when realizes that some species are reliant on some representation under which they are no viable then a target might be imposed. This impacts especially the species presenting the shortest favourable areas, given that if targets are too high then they are not achieved whilst removing resources from other species. Therefore, these species would be better accommodated in plans that account for the value of under-representation.

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Climate change concerned conservation corridors (C5 for short) is a very operative paradigm for the natural re-alignments of species ranges to shuffles in geographic climatic patterns. C5 invoke a time-aligned path for a species from a starting time to an ending-time in which species persistence may be appraised. C5 were originally introduced in the conservation literature by Paul Williams and colleagues (2005) under the name of dispersal corridors. We renamed and refined its use in order to give them a spatio-temporal dimension and to span an array of distinct conservation problems (for single and multiple species), respectively.

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C5 depend on three types of data that might be accessible under multiple methods:

• local climatic/environmental suitability for each species under an array of distinct time periods;

• a dispersal model that measures the success of species movements, and

• information on local cost (varying with time) that characterizes how difficult is an area to be used by species or, alternatively, to be set aside or controlled for conservation purposes.

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Different from other approaches that integrate climate change concerns in area prioritization, C5 have been presented as a conservation tool based on a spatio-temporal unit defined based on the dispersal abilities of species. As a unit, C5 brings the advantage of considering to where the conservation value of an area (or its loss) may be guided for in the future. Moreover any twocorridors presented for a species do only sum up  species persistence if they are assumed independent (i.e. they form pathways that do not coincide in the same area at the same time-period). This independency constraint brings extra complexity to area evaluation as the number of potential pairs of corridors to be selected for each single species is typically prohibitively large.

The C5 framework is flexible and integrates different conservation considerations. It enables to schedule area usage to:

• identify in where the persistence of a single species is maximized as it adapts to climate change;

• maximize the probability of persistence of a set species as a whole, in a given area amount spread into time;

• maximize the summed persistence of species after establishing a persistence target, under a budgetary constraint;

• minimize cost (or socio-economic conflicts) in where persistence targets for the species are fulfilled;

• minimize the summed gaps (among species) to the established persistence targets, under a budgetary constraint;

• minimize the summed gaps (among species) while a given number of species have to be adequately represented concerning their persistence targets. Once again a budgetary constrained limits area selection.

 

These distinct approaches present a range of scenarios in which planners play in a trade-off between solution controllability (by setting targets) and solution feasibility (capacity to obtain optimized solutions). Related to these basic problems there are multiple conceptual approaches able to be prepared for more particular case-studies.

 

It is in this crossing over of model development and model implementation (for the academic or policy sectors) that I am focusing in the last years.