How do you know that protected areas are actually protecting anything? Use Matching!

This is the sixth in a series of weekly blog posts covering conservation topics with a focus on protected areas and the laws and institutions that support them (or don’t).

The forests surrounding many protected areas are being rapidly cleared or degraded. Shown is recent deforestation for oil palm plantations along the edge of Bukit Palong National Park in Peninsular Malaysia. Photo by William Laurance,

The forests surrounding many protected areas are being rapidly cleared or degraded. Shown is recent deforestation for oil palm plantations along the edge of Bukit Palong National Park in Peninsular Malaysia. Photo by William Laurance,

A new methodological approach is gaining traction in the conservation science community. This method is borrowed from the economics literature and has historically been used to analyze the impact of policies on future wages, socioeconomic status, or other economic variables. Today, conservation researchers are beginning to employ this rigorous approach – known as matching – for a variety of purposes. One prevalent application of matching is the evaluation of protected areas, both terrestrial and marine. Why is the matching approach so popular to evaluate protected areas? First of all, the need to rigorously evaluate protected area performance is becoming more widely acknowledged in the literature. The Convention on Biological Diversity includes targets about national designation of protected areas which should be effectively managed. Without monitoring and evaluation of these lands and waters, there would be no way to determine whether management were effective and whether the targets were met. 

Let’s suppose that you are interested in whether the protected areas in your country are preventing the loss of forests. Ok great. Can you simply compare the deforestation rates inside and outside the protected area? Unfortunately, that won’t be completely accurate. The lands inside and outside of that particular protected area might have different types of vegetation, terrain, or soil productivity. Because of these inherent differences, you cannot simply compare the inside to the outside. Ok, what about comparing the protected area before it was established to after? That is a bit better, because it is focused on the same piece of land. However, it doesn’t accurately capture the impact of protection because there may be other factors at play in this location. Perhaps other policies were enacted around the same time that affected deforestation.

inside outside

Ok, so how do we actually isolate the IMPACT of the protected area (the legal establishment itself) on deforestation? One way to do this is to use matching. To conduct matching, the researcher selects a piece of land that is protected and a similar (matching) piece of land that is NOT protected. The protected area is considered the “treatment” and the unprotected area is the “control.” Comparing the treatment and control to each other is a fair and simple way to quantify the benefits of protection. Hence, matching isolates the impact of the protection policy ITSELF and rules out extraneous factors that could affect the results (like other policies, the impacts of different landscape types or soil types, etc).

inside outside

How do you know if two pieces of land (or water) are a good match? Use covariates! Covariates are variables that correlate with the treatment and the outcome. It is necessary to use covariates because the location of protection or deforestation (or whatever outcome variable you are looking at) on the landscape is not random. Protected lands tend to be placed in isolated, mountainous areas with low soil productivity – high and far from development or urban areas. Also, deforestation occurs happen more often in places that are easier to access – closer to roads or other access infrastructure and also closer to the forest edge. By accounting for these factors using covariates (distance to roads, distance to forest edge, etc), the researcher can select well-matched control regions that are actually comparable to the protected area in question. This reduces bias in the analysis and allows you to compare apples to apples.  Further, it is worth noting that when using matching, the magnitude of the impact is likely to be much smaller than the estimate of impact using more traditional approaches simply because the covariates are accounted for.

What are some limitations of the matching approach? Simply put, sometimes you cannot find a perfect match. There may not always exist enough similar parcels on the landscape that are similar in access and topography to your protected area. Also, there is the issue of choosing covariates. How do you know that you have chosen the correct covariates or a sufficient number of them? As more researchers use matching, the scientific community will gain a more refined understanding of which covariates to use and when.

What research frontiers exist for applying matching in conservation? There are many! Matching could be used to evaluate conservation interventions other than protected areas – matching has been applied to quantify impacts of payments for ecosystem services, for example, The method could also be extended to indigenous reserves, community based natural resource management areas, or other area-based interventions. The application of matching to evaluate protected area downgrading, downsizing, and degazettement (PADDD) is an area of research that merits exploration. Using matching can help answer questions like – what is the impact of changing a protected areas’ status on carbon storage for climate mitigation? What is the impact of reducing a protected area’s size on biodiversity? If a protected area’s protection is removed, how is land cover affected? There are endless possible areas of research and exploration that could employ matching.

What are some tools and resources to help with matching? There is a wealth of literature available on matching and impact evaluation – see here, here, and here.  There are also some R packages – MatchIt and Matching are two examples. If you are aware of other resources, post a comment below!

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