Underwood et al. (2010). Identifying conservation areas on the basis of alternative distribution data sets

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On December 3rd, we discussed the paper by Underwood et al. (2010). They take up an important issue related to systematic conservation planning, that is the distribution data used as a basis of reserve selection. If distribution data, as in this case, is relatively incomprehensive, reserve networks based on data may become very large (and thus very expensive), as there are many omissions and little flexibility.  Using predicted distribution data aims at filling information gaps by detecting locations where a given species is likely to occur. This is supposed to add flexibility into the reserve selection and get rid of (at least a part of) the omissions in the original data. On the other hand, if there is large uncertainty in the predicted distributions of species and not the possibility to validate them, reserve selection with predicted distributions runs the risk of assigning priorities to locations where species do not actually occur.

Underwood et al. present two approaches to balance this tradeoff. First, at the modelling stage, they limit the predicted occurrences into the proximity of observed occurrences of species. They call these dispersal-limited predictions, as they constrain the predicted occurrences into locations which are considered to be within reach from the confirmed occurrences in the original data. Although this approach may reduce the risk of commission error, it leaves the predictions more sensitive to spatial bias in data collection than predictions made throughout the area. They compare the reserve networks based on dispersal-limited prediction data with ones based on the original observation data set and with predicted occurrences (Boolean vectors based on environmental variables) with no spatial constraints.

Second, they applied what they call the adaptive method in the reserve selection stage. The adaptive method is a nested selection scheme where 50% of conservation target for each species has to be met using the occurrence data, 30% is met using the dispersal-limited predicted data, and 20% can be met using predicted occurrences anywhere in the landscape. This is a very interesting approach, and we wished Underwood et al. had discussed the implications of their approach a bit more in depth. We were wondering if the occurrence data and predicted distributions Underwood et al. use are suitable for testing the approach further. As their data does not allow for confirming the predicted occurrences, it is not possible to compare the solutions in terms of their true efficiency. But it would be interesting and should be relatively easy to test their method with, for example, simulated data.

Underwood et al. show, importantly, that the type of data used in reserve selection has a great impact on the outcome, and the choice of whether one should use observation data or predicted occurrences is not a trivial one. Their reserve networks based on the alternative data sets had remarkably little overlap, although a small fraction of cells showing high irreplaceability were nearly always selected. Their results also show clearly, that when reserve selection is based on targets, the formulation of targets has a great impact on the available options from which the software can select conservation priorities from.

Full reference: Underwood, J.G., D’Agrosa, C. & Gerber, L.R. 2010. Identifying conservation areas on the basis of alternative distribution data sets. Conservation Biology 24: 162-170.
http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2009.01303.x/pdf

Knight et al. 2010. Mapping Human and Social Dimensions of Conservation Opportunity for the Scheduling of Conservation Action on Private Land

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The paper by Knight et al. (2010), discussed on 5th November 2010, was an interesting piece of research to read and stimulated active and intense discussion among the group of conservation biologists present. The paper is interesting as it is the first (the novelty side was well emphasized by the authors across the publication) to try and incorporate the social dimension into the planning process to prioritize conservation effort. As authors point out, social factors are an important component that can greatly affect effectiveness of conservation effort and that has largely been overlooked in the past, despite the increasing need to deal with the human dimension as large tracts of land important for biodiversity in many countries, including Finland, are nowadays privately owned.

The issue was untangled by the authors by means of reporting a case study where, in order to quantify conservation opportunities, a questionnaire targeted at local stakeholders was used to map the social dimension in an area of high biodiversity value. The general idea of trying to incorporate social factors into conservation planning was well appreciated within the “conversation planning” group. It was also interesting to know how, from the questionnaire data, information on opportunities for conservation can be derived by ranking landowners into several categories, with the highest providing the best opportunity and thus should be the first to target in order to increase public participation and support to conservation initiatives.

However, the authors have taken the whole study on a very general level, thus lacking details on how this new social dimension could be included when planning conservation in a real world where more heterogeneous conditions exist (in terms of spacing of biodiversity and social factors) on a larger scale than the localized and rather homogeneous area considered in the study. What are the tools available, or to be implemented in the future, to achieve more cost-effective conservation based also on the social dimension is not clarified in the paper. In fact, the effectiveness of considering this new aspect is not assessed with respect to achieving conservation goals. How the landowners would behave when put in front of the real condition to decide how to manage their land is not discussed.

The role of the social dimension was at times overemphasized at the expenses of ecological knowledge and conservation planning, with the latter seemingly relegated to a secondary position when dealing with conservation on private land, while the former being the first option to consider. The strict divide and ranking of these two groups of dimensions was not appreciated, as the common belief is that both are important and can complement each other, and that the conservation planning, based on cost and ecological data, can be updated and implemented in a very flexible and dynamic process based on information derived from the social dimension. The question arising naturally from this reading is: how to get reliable proxies, if ever possible, from available and accessible spatial data to represent the social dimension and thus implement the effectiveness of conservation planning at the global scale? Any ideas on this would be greatly appreciated.

Here you can find the paper:

http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2010.01494.x/abstract

Nagoya COP10 outcomes

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For Friday the 12th of November we had a bit of a special issue in our weekly journal club. Many of us had been somewhat following what was going on in Nagoya CDB COP10 meeting that took place in Nagoya, Japan between 18th and 29th of October. Following the over all failure of global 2010 targets it is, after all, very interesting to see what the global high-ranking political community has in mind for the next decade.

Since none of us in our little club was high-ranking enough to have actually been in Nagoya, we had to settle for the Advance Unedited Texts from the meeting website. These documents listing several important outcomes of the meeting have no doubt passed through many delegates and represent a compromise everybody is happy with. It is no surprise then that we didn’t find the documents very easy to digest. Right after the meeting both international as national (Finnish) media praised the success of the meeting, but at least the press releases said very little about what was actually achieved. After reading some of the documents we knew a little more, but not a lot. As expected, the outcomes are more of declaration than an action plan, although important targets are listed as well. Since there are no binding obligations it is left up to national implementation to actually meet the targets and it that sense we still have to wait and see how the implementation takes off. Needles to say, after the 2010 failure at least our journal club wasn’t overly optimistic about meeting the targets.

Nevertheless, several important targets are listed. Few of these attracted more discussion, like target 1

By 2020, at the latest, people are aware of the values of biodiversity and the steps they can take to conserve and use it sustainably

which is perhaps the most important target of them all, but at the same time a bit funny: how can the other targets be met if this as a starting point won’t take place until the end of the target period? Target 5 states that

By 2020, the rate of loss of all natural habitats, including forests, is at least halved and where feasible brought close to zero, and degradation and fragmentation is significantly reduced

Clearly the wordings are more careful than 10 years ago, in other words instead of speaking about stopping we are talking about halving and reducing. Perhaps the most interesting target is the target 11 which gives us some concrete numbers

By 2020, at least 17 per cent of terrestrial and inland water, and 10 per cent of coastal and marine areas, especially areas of particular importance for biodiversity and ecosystem services, are conserved through effectively and equitably managed, ecologically representative and well connected systems of protected areas and other effective area-based conservation measures, and integrated into the wider landscape and seascapes

This sounds all very good – or at least better than the current situation – but still much is left to how one actually calculates the statistics. For example, in Finland the proportion of protected forests and areas under restricted forestry use is 14.5% of the total land area, which is not that far off from the target. However, if we consider strictly protected forests on actual forest land the number drops to 5.2%. Also it remains to be seen how “equitably managed”, “ecologically representative” and “well connected” systems of protected areas are implemented in reality. The upside is that considering these targets, there is clear need for conservation planning in the coming years 🙂

There has been a clear sea-change in the way how the motivation of biodiversity conservation is presented. Ecosystem services and especially TEEB are very pervasive in the documents, which pretty much reflects what is going on in the world of conservation today. If you can’t appeal to people’s heart, appeal to their wallet.

Further into our discussion we also agreed that human population growth is almost nowhere to be found in any of the texts. Economic growth and it’s harmful effects are lightly discussed, but there is no mention about the detrimental effects that ever growing human population has on the environment and biodiversity. The reason might be that it still is too touchy a subject especially for some of the developing countries. On the other hand, at least from the perspective of resource consumption it is the excessive standard of living in the developed world that is causing a big strain on the environment.

In the end we felt that perhaps it is still a bit early to call the verdict on Nagoya as so much depends on how the “flexible framework” is utilized and implemented in different parts of the world. At least the meeting wasn’t as immediate failure as Copenhagen climate negotiations were, but already very critical views have been published concerning the outcome of COP10. We’ll definitely keep an eye on how things are progressing.