Habitat heterogeneity must play a significant role in determining the occurrence, amount and persistence of biodiversity. Well, at least this is what our gut feeling is telling us. Reality, however, seems to be that we now very little about the effects of heterogeneity in different environments and next to nothing generalizable over different environments and scales. Indeed, as Tews et al. (2004) pointed out in their highly sited review, there seems to be a positive correlation between habitat heterogeneity and animal species diversity, but it’s all very species and scale dependent. Furthermore, studies have tended to be very biased towards certain species groups and environments.

Agricultural landscapes are probably one of the more studied environments. In this week’s journal club we read through Fahrig et al. (2010): “Functional landscape heterogeneity and animal biodiversity in agricultural landscapes”. Many of us had hoped for a review-type paper pulling together information on empirical work that has been carried out in agricultural landscapes. Equally, after reading the paper many of us felt a little baffled by the scope of the paper as it turned out not to be a review, but rather a “Idea and Perspective” type of contribution. Authors’ describe their objectives threefold (shortened from the original):

1. to develop a conceptual framework for the study on landscape heterogeneity in the context of agricultural landscapes
2. identifying three important unanswered questions about the relationship between landscape heterogeneity and biodiversity in agricultural landscapes
3. to suggest a general methodological approach for studies to address aforementioned questions

Our greatest complaint about the paper was a general vagueness we felt it has. This may be partly unjustified as it is a perspective paper, but still we would have appreciated more literature (and especially empirical studies) sited, and clearer connection to policy making and implementation. Neither the conceptual framework nor the proposed study questions struck us particularly novel. Dividing heterogeneity into two main components – compositional and configurational – is useful, but we felt that this is generally the way heterogeneity has been treated previously as well. The division between structural and functional heterogeneity reminded us very much about the treatment of connectivity popular especially in the context of landscape ecology.

Far more interesting aspect of the paper is the discussion on what sort of effects does heterogeneity have in different kinds of landscapes and where should it be promoted from biodiversity conservation point of view. These questions are formulated as the three research questions the authors are proposing. Does increasing heterogeneity (or the different components of heterogeneity) increase biodiversity both in more-natural environments as well as in production environments? To put it differently, is increasing heterogeneity more beneficial in more-natural or production kinds of environments? Also, what levels of heterogeneity are desirable? The latter question the authors address by stating that often a times intermediate levels of heterogeneity seem to result in the most biodiverse environments. This observation is similar to that of classical Intermediate Disturbance Hypothesis in ecology.

On the policy side Fahrig et al. emphasize that

“agri-environment policy should aim to enhance biodiversity to the extent possible while still providing agricultural products for human consumption”

and that

“Policies aimed at increasing the heterogeneity […] thus reducing agricultural production, will frequently be considered unacceptable by farmers”

Authors are thus emphasizing the main function of agriculture: food production. Given the heavy subsidization of agriculture especially in the EU and the US, it seems the there would be more space for agri-environment schemes and economical instruments. Farmers pay check does not come only from selling revenues, but to a great extent from different subsidies. Therefore decreased production might be acceptable if the loss is compensated in some other manner.

In conlusion, the paper was an interesting read, but an unnecessary lengthy one and from a bit different perspective that at least some of us had hoped for. Obviously more research is needed before we can really say something generalizable on the effect of habitat heterogeneity on the amount in biodiversity, in agricultural landscapes or elsewhere.

Full reference: Fahrig L, Baudry J, Brotons L, et al. 2010: Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecology letters. Link to paper

Extra reference: Tews J, Brose U, Grimm V, et al 2004: Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of Biogeography. 2004;31:79-92.

On December 3^rd, 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

The paper by Knight et al. (2010), discussed on 5^th 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