In her paper from 2007, Elinor Ostrom calls for diagnostic frameworks for identifying key factors of (sustainable) resource use rather than attempts to prescribe simple, universal solutions to complex problems. We chose this paper, because after Walker’s paper from last week we were left curious about the community-driven system management that did not seem to follow Walker’s model of costs, risks and benefits. Therefore, we went for more insight about community-managed social-ecological systems (SESs).

Ostrom describes SESs with a hierarchical framework, where variables can be decomposed and analysed. At the highest level, there are 6 variables that interact with each other: Resource system (RS), Resource units (RU), Interactions (I), Governance systems (GS), Users (U) and Outcomes (O). These are placed in certain social, economic, and political settings and further interact with related ecosystems. All highest-level variables can be decomposed into second-tier variables: RS can have properties such as clarity of system boundaries, size, productivity and location, RU may be characterized e.g. by mobility, growth rate, economic value or spatial and temporal distribution, and so on. Ostrom lists 52 second-tier variables. The significance of the variables in determining whether the SES is sustainable or not is highly variable between different systems.

There are a number of very interesting example cases, for which the key determining factors of sustainability were outlined. The framework reveals, for example, that Garrett Hardin’s case of the tragedy of the commons is hardly a general case but rather has quite specific assumptions about the system.

As people who try to capture important factors and turn them into models that describe phenomena at general levels, some of us were intimidated by the idea that every case should be treated separately. We were repeatedly caught trying to identify some general features that would explain why some systems are sustainable and others are not. Transparency of the governance and repeated face-to-face contact with other resource users seemed to be examples of these. We were thinking about the amount of work required if every SES was to be decomposed and described separately. On the other hand, every conservation management plan is unique anyway, and identifying the key factors in each case would seem like a reasonable thing to do in the context of management planning.

We were wondering how well the framework could be used for forecasting how a system might develop. For example, Ostrom points out the problem of “roving bandits” that destroy resources in one place and move on to another one. In this kind of situation, local institutions are often too slow to develop and implement strategies to prevent the grossly unsustainable use of resources. For this kind of cases a framework would be useful that would enable the local community to foresee and prevent the situation. We were also left curious about the implications: what to do after the key factors are identified?

Link to the paper:
Ostrom, E. 2007. A diagnostic approach for going beyond panaceas. – PNAS 104: 15181-15187.

Protected areas are not always successful in conserving biodiversity. Unsustainable resource extraction and illegal activities inside protected areas often occur. Can factors that make protected areas vulnerable be identified and best management strategies found?

Studies addressing these questions should be of interest to conservation biologists. According to Walker the importance of law enforcement and monitoring in the successful management of protected areas has been underscored throughout the conservation literature, even if many empirical studies have found them to be strongly correlated with resource condition. Walker addresses these questions with a law-enforcement and monitoring game-theory model from the political science and commons literature. The three key factors evaluated are:

• cost of monitoring rule breakers
• benefit of catching a rule breaker
• probability of catching a rule breaker

Assigning these variables realistic values proved to be difficult but even with crude values the variables had a remarkably strong predictive power and agreed surprisingly well with the reported outcomes in 116 protected areas from the peer-reviewed literature. The main finding was that conservation is unlikely to succeed if the cost of monitoring were greater than the product of the probability to catch a rule breaker and the benefit of doing so. Surprisingly, for me, was that the variables driving the monitor’s decision are those that most often determine the outcome because the resource users make their decisions according to what they think the monitors will do. So, by using either carrot or stick strategies to punish or motivate resource users little will be achieved in terms of stopping illegal use. This is valuable to keep in mind.

I think many of us felt the paper was a bit heavy to read because we were not familiar with the methodology or the terminology. The structure of first presenting the model in depth and then using it to predict case study outcomes can be confusing but for my part I really appreciated that so much space was given to explain the theoretical model in depth. It was also interesting how very different terminology political scientists and conservation biologist are using and this might in some respect mirror how we value things (e.g. the paper consistently talked about “resources” instead of for example “biodiversity features” etc.).

One concern we had with the paper was that it only considered top-down managed protected areas (which in a way was useful for us doing conservation because most national parks and reserves fall under this category even though other forms of management exist). Of course, everything cannot be covered in just one publication and the topic was clearly defined from the beginning. Related to this it would also have been interesting to hear more about the outcomes (in F&G) when transferring monitoring to the community, the so-called “pass the buck” strategy.

We also discussed if the reality is this black and white? And somehow this lead us to consider the problems we have even in Finland with illegal poaching of wolves and how this, according to the predictions of the paper, should be solved.

Conclusion: We agree that conservation biologists should be more aware of the work done by political scientists to solve monitoring dilemmas. In fact, we were so inspired by this new angle of approach that we decided to read some more of the references by Ostrom and others (we also recollected that we touched upon this subject already in November 2009 when reading about socio-ecological systems, see previous contribution by Heini).

Link to the paper:

Walker, K. L. 2009. Protected-Area Monitoring Dilemmas: a New Tool to Assess Success. Conservation Biology 23: 1294-1303.

doi: 10.1111/j.1523-1739.2009.01203.x

Do you work with species distribution modelling from the perspective of climate change? In that case, this paper is FOR YOU. Working with species distribution, but don’t really care about climate change? This paper is still FOR YOU.

In this article Buisson and colleagues tackle the fundamental problem of uncertainty in projecting species distribution shifts under climate change. The work is pretty breath taking: 35 species, 100 partitions of data, 7 different statistical modelling methods, 3 climate models, 4 emission scenarios and 3 different time steps (2020, 2050 and 2080), resulting in a staggering 8 400 different projection per species per time step. Not to mention the numerous PCAs and one phylogenetic tree done within this study.

This paper shows, statistically quite convincingly, that the greatest contributors to the variation in model results are in fact the statistical methods we use for projecting the likelihood of a species to occur in given climate conditions. Thus, the selection of modelling method, or even the decision of using just one instead of several, should be done with care. Unfortunately this paper does not go on by making any suggestions about which methods to choose, but instead underlines the need of using several modelling methods and presenting not just the results across them, but also the uncertainty related to them.

Buisson and colleagues have really done a comprehensive and good work: The paper is full with technical details, tests and analyses, but the text has been written so that the reader does not get lost in the jungle of residuals, autocorrelations and standard deviations. The only weakness we could find was the fact that the individual model performance and how this relates to the high variation in model results was not reported. Reading between the lines it could be that the authors have decided to save this the sequel. And we can’t really blame them – this paper definitely had information worth of several papers. Excellent job!

Final conclusions: You have to read this :)!

Buisson, L., Thuiller, W., Casajus, N., Lek, S. and Grenouillet, G. (2010) Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16, 1147-1157.