Can designation without regulation preserve land in the face of urbanization? A case study of ZNIEFFs in the Paris region

Applied Geography 45 (2013) 342e352

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Can designation without regulation preserve land in the face of urbanization? A case study of ZNIEFFs in the Paris region Anne Mimet a, b, *, Richard Raymond a, Laurent Simon a, Romain Julliard b a b

UMR CNRS-Paris 1- Paris 7- Paris 8- Paris 10, Laboratoire Dynamiques Sociales et Recomposition des Espaces, 2, rue Valette, FR-75005 Paris, France UMR MNHN-CNRS-UPMC, UMR 7204, Centre d’Ecologie et des Sciences de la Conservation, 55 rue Buffon, FR-75005 Paris, France

a b s t r a c t Keywords: Natural area Conservation Land cover changes Isolation Urbanization Policies

Preservation through legal protection of natural areas is costly and limited in its extent. A much cheaper strategy to preserve natural areas could be to simply identify areas of ecological interest and let it be known. A survey was launched in France to identify such areas (called ZNIEFF) in the early 1980s. Since then, municipalities have had to account for ZNIEFFs in their formal land planning schemes, though they are not under any obligation to protect them. In this study, we tested the effectiveness of ZNIEFFs as a conservation tool in an area of high growth near Paris. Using GAM modeling, we compared the rate of urbanization inside and outside ZNIEFFs in the 17 years following designation, accounting for the share of farmland, the overall rate of urbanization and ZNIEFF proportion in the municipalities, and also accounting for demography and physical constraints (hydromorphy and slope). Overall, there was less urbanization inside ZNIEFFs, but this varied depending on the context. Surprisingly, they were better preserved in areas of more intense urbanization. This effect was increased if farmland area was already reduced (<30% of the municipality area). In contrast, when farmland was still predominant and urbanization rates were lower, ZNIEFFs tended to be more urbanized than the areas outside. This shows that the value of remnant natural areas varies considerably, perhaps as a function of the value attributed to farmland. Ó 2013 Elsevier Ltd. All rights reserved.

Introduction The tools created to conserve natural systems control the management of protected areas and the human activities they support to some degree. The first reserves were created to protect natural areas of special interest in the nineteenth century. Fontainebleau was created as the world’s first reserve in 1861, and Yellowstone National Park was America’s first national park, which was established in 1872. In these reserves, human activities are prohibited or limited by law. However, areas under any sort of protection represented only 20% of French territory in 2010 (Lefebvre, 2010). A large proportion of natural and seminatural areas, which host an important part of the nation’s biodiversity and ecosystem services, is not protected by law and

* Corresponding author. UMR CNRS-Paris 1- Paris 7- Paris 8- Paris 10, Laboratoire Dynamiques Sociales et Recomposition des Espaces, 2, rue Valette, FR-75005 Paris, France. Tel.: þ33 662314471. E-mail addresses: [email protected] (A. Mimet), [email protected] (R. Raymond), [email protected] (L. Simon), [email protected] (R. Julliard). 0143-6228/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apgeog.2013.10.001

could be subject to human pressure (Mathevet & Tamisier, 2002), especially urbanization (McDonald, Kareiv, & Forman, 2008). During the twentieth century, the method of completely protecting natural areas with prescribed controls reached its limits. Although the effective protection of natural areas can prevent their degradation and preserve common and rare species (Bruner, Gullison, Rice, & da Fonseca Gustavo, 2001; Devictor, Godet, Julliard, Couvet, & Jiguet, 2007; Pfaff, Robalino, Sanchez-azofeifa, & Andam, 2009; Sanchez-Azofeifa, Daily, Pfaff, Sa, & Busch, 2003), biodiversity loss continues despite an increase in strictly protected areas (Millennium Ecosystem Assessment, 2005). From a social point of view, the delineation and creation of a protected area may not be supported by local landowners and residents, because the restrictions and prohibitions of activities can hinder development (Abakerli, 2001). Particularly in highly urbanized areas, legal constraints may be unwelcome because they restrict land use and may be perceived as preventing development. However, in these areas, the recognition of the natural characteristics of an area may be used in the promotion of a natural resource’s ability to support local development.

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To address these conservation issues and reconcile human and ecological needs, a new approach to conservation arose in the 1990s. Conservation objectives were integrated into local politics and land use planning (Crumpacker, 1998). To complement the legal framework (hard laws) of the reserves, managers developed more flexible tools (soft laws). Partnerships have been developed between the public agencies that are responsible for protecting the interests of the general public, and interested local stakeholders (Hage, Leroy, & Petersen, 2012; Streck, 2004; United Nations Environment Programme International Union for the Conservation of Biodiversity and the Convention on Biodiversity, 2006). Many of these measures take the form of contracts that are written and negotiated locally. Programs may be publically funded to counterbalance the economic effects of environmentally friendly practices, such as agri-environmental schemes in Europe (Burton & Paragahawewa, 2011) and rural legacies in Maryland (US) (Lynch & Liu, 2007), under the “Payments for Environmental Services” concept (Simpson & Sedjo, 1996). With this new conservation approach, global conservation issues are adapted to local and regional conservation projects, and nature is promoted and valued as a local resource. These new tools are flexible and adaptable. They are used for local development and allow the protection of a more common biodiversity. (Locke & Dearden, 2005). These tools allow for the integration of nature and society into land use planning (Rosenzweig, 2003) and return regional nature conservation to local governments, promoting acceptance and cooperation from the population (Downsborough, Shacketon, & Knight, 2011; Khan & Bhagwat, 2010). However, in this integrated vision of nature and human interests, conservation depends on the value assigned to natural areas. The efficiency of such tools must still be studied because biodiversity issues are not always properly taken into account in land use planning. Recent actions to protect biodiversity in the new context of sustainable development are poorly understood, even in situations involving high degrees of human pressure (Kates et al., 2001). Some studies examining the links between biodiversity and society have focused on spatial dynamics because space is the common dimension of these two aspects. These studies seek to understand how society (in terms of its social, demographic and economic characteristics) modifies biodiversity (defined in terms of space or species). They also seek to develop management tools to enhance and facilitate the integration of biodiversity issues in land use planning (Bürgi, Hersperger, & Schneeberger, 2004; Haberl et al., 2008). In this context of developing new conservation tools (soft laws and economic tools) that integrate social constraints, there has to date been no exploration of cognitive tools, i.e., the way the mere designation of a natural area can influence the development of an area. In France, the designation of a natural area, apart from any legal protection or financial support (Lynch & Liu, 2007), is called a ZNIEFF (Natural Zones of Ecological, Faunistic and Floristic Interest). We examined the geographic conditions influencing the fate of the natural designated areas (ZNIEFFs) and thus the effect of designation alone on preserving biodiversity from urbanization. We focused our study on Seine-et-Marne, to the east of Paris. The area harbors very important and contrasting human pressures ranging from urbanization (due to the proximity of Paris) to a tradition of agriculture. We used the difference between the rates of urbanization in ZNIEFFs and outside them over the period 1982e 1999 as an indicator of ZNIEFF preservation. We chose changes in urbanization because of its strong negative consequences for biodiversity (McDonald et al., 2008) and importance in the Seineet-Marne region during the study period (Mimet, Houet, Julliard, & Simon, 2013). Using a general additive model (GAM), we

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examined actions that were correlated to the observed differences in urbanization: physical context (hydrological characteristics, slope and proportion of ZNIEFF area in the municipality), land cover before ZNIEFF designation (proportions of farmland and urban areas in the municipality) and human pressures on the municipality during the time of the study (demographic pressures and changes in urbanization in the municipality). From among these factors, we identified the conditions most related to preservation and urbanization of the designated areas. Material and methods ZNIEFF, a French designation tool for natural areas of ecological interest ZNIEFFs were established in France in the early 1980s. They represent one of the oldest nature designation tools in France, and they identify areas of ecological or biological interest. Today, there are 1690 protected areas and 14,836 ZNIEFFs in France. The strongly protected areas cover less than 2% of metropolitan areas, while ZNIEFFs cover approximately 25% (IUCN, 2010). The ZNIEFF designation has no legal protective value. No statutory constraint governs the management of ZNIEFFs, and human activities are not constrained in ZNIEFFs. They are not financially incentivized. The ZNIEFF designation is simply an informative tool based on the recognition of the ecological interest of an area (Clap, 2005). Despite having no legal protective status and no legal support, ZNIEFFs play an important part in local policy-making and landplanning policies (Couderchet & Amelot, 2010; ElissaldeVidement L., Horellou, Humbert, & Moret, 2004; Mathevet & Lepart, 2013), mainly because since 1993, they have had to be recognized by regional government authorities, and because regional government authorities must ensure the protection of natural areas (Clap, 2005). At the local scale, municipalities must refer to the ZNIEFF inventory in relation to urban land use planning. They thus constitute an important database of ecological areas, one that is available for the entire French territory. Study site This study took place in the eastern part of the Ile-de-France region, which contains the Paris metropolitan area. The Ile-deFrance region comprises less than 2% of the national territory but is home to 19% of the French population. Its rate of population growth (þ0.9% per year) is higher than that of the country as a whole (þ0.47% per year) (INSEE, 2013). Our study focuses on the Seine-et-Marne region, which is the largest administrative region within Ile-de-France and accounts for 5915 km2, 49% of its area (Fig. 1). The study area hosts a range of land covers, from a highly urbanized area in the west to rural and agricultural areas in the east. The demography of Seine-et-Marne region is highly dynamic: its population has doubled in the last 30 years. In 2007, its population was nearly 1,290,000 inhabitants, and the population density was 218 inhabitants per km2. Farmland remains the dominant land cover, with more than 60% of the territory allocated to agricultural activities. The proximity of the study area to Paris has resulted in substantial land cover changes between 1982 and 2003, dominated by urbanization (þ2.2% of urban areas, reaching 10.45% of the territory in 2003), development of transportation networks (þ0.3%), and fragmentation and loss of agricultural areas (2.5% of farmland) and forest areas (2.1% of forested areas). Natural areas remain in some locations, essentially consisting of forested areas and wetlands (22.24% of the study area in 2003) (data from IAU, see below).

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Fig. 1. Municipalities, ZNIEFF and land cover in the Seine-et-Marne region in France.

Land cover and environmental data In this study, we used land cover datasets from 1982 to 1999 that were provided by the Institute of Urban Planning and Development of the Ile-de-France region (IAU). Vector maps were derived from interpretation of aerial photographs and additional data, such as administrative files and information from municipalities on building types and land uses. Because the technical quality and resolution of the land cover data improved between 1982 and 2003, the classification was actualized by IAU so that the same classes were used for both years. The resolution of the land cover data was estimated by IAU to be 1/5000 for a minimum mapping unit of 625 m2. The initial nomenclature (83 land cover classes) was simplified into five classes: agricultural areas, urbanized areas and transport, forests, open natural areas and wetlands. The urbanized

areas and transport class included buildings and roads as well as parks and gardens. We used the hydrological network (BD-Carthage, Institut Géographique National) to define the hydrological conditions of the municipalities. We used a digital elevation model from the SRTM (125-m resolution) to compute the average slopes of the municipalities. Data transformations from the various databases were conducted using ArcGis Desktop 9.3 (ESRI, Redlands, CA, USA). ZNIEFFs, municipalities, and urbanization in Seine-et-Marne between 1982 and 1999 In Seine-et-Marne, the ZNIEFFs were identified between 1982 and 1984. Before 1999, Seine-et-Marne contained 222 ZNIEFFs with a total area of 978 km2, or 16.5% of the study area. The ZNIEFF is the

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Fig. 2. Response of the dependent variable ZNIEFF-evol to three significant explanatory variables (a eDemo, d e Urba_Press and c e interaction between Farm82 and %ZNIEFF). In c -, the value of ZNIEFF-EVOL is indicated in grayscale. The darkest grays are combinations of ZNIEFF and farmland proportions that favor ZNIEFF urbanization. The lightest grays are combinations of ZNIEFF and farmland proportions that favor preservation. The dotted lines represent the standard error of the average value (the solid line).

most common status of natural areas that are designated or protected in the study area. We studied the land cover changes before and after ZNIEFF designation, from 1982 to 1999. Land cover in 1982 could not have been influenced by ZNIEFF, and until 1999, no additional designation and protection tools (even economic) were established in Seine-et-Marne except for some small local protected areas specific to French law (“arrêté préfectoral de biotope”, legifrance, 2013). Thus, we could be confident of analyzing the effects of ZNIEFF designation and not those of other protection tools. As already discussed, ZNIEFFs have no legal protection status, but municipalities must refer to the ZNIEFF inventory in their land use planning. In the present study, the municipality was considered the statistical analysis unit. The municipality scale is relevant for three main reasons. First, decisions concerning local land planning are made by municipalities. Second, building permits are granted by municipalities. Third, many social issues and needs are handled by municipalities. Since ZNIEFF designation, municipalities have been required to settle any development and land planning conflicts involving ZNIEFFs by arbitration. Most municipalities have formal land schemes for future land planning. Thus, they can allow either the development or the preservation of ZNIEFFs. Overall, 218 of 514 municipalities in the study area have a ZNIEFF in operation (i.e., more than 1% of the area of the municipality is designated as a ZNIEFF). Of these 218 municipalities, we only considered those that were engaged in a land scheme before 1990 to avoid the effect of land scheme engagement. We also excluded the municipality of Fontainebleau from the analysis, as this municipality has housed

the biological reserve of Fontainebleau since 1953, and we excluded municipalities with an “Arrêté préfectoral de Biotope” between 1982 and 1999. After these exclusions, 145 municipalities were included in the analysis. Initial hypotheses and model construction Response variable Changes in urbanization were used as indicators of the ecological degradation of natural areas for two main reasons. First, urbanization has been identified as major cause of biodiversity decline and biological homogenization (Mckinney, 2005). Second, changes in urbanization were the driving force in land cover dynamics in the Seine-et-Marne region between 1982 and 1999 (Mimet et al., 2013). The response variable reflects the differences in urbanization between the inside and the outside of ZNIEFFs. This variable is termed ZNIEFF-evol. The territory of each of the 145 municipalities in the study was divided into areas situated in the ZNIEFF and those outside the ZNIEFF. The urbanization rate between 1982 and 1999 for each of these two areas was computed based on land cover data. The response variable was then computed as the difference between the urbanization rates outside and inside the ZNIEFF for each municipality between 1982 (before designation) and 1999 (15e17 years after the designation) (Fig. 2). Positive values of ZNIEFF-evol indicate higher urbanization rates outside the ZNIEFF, and higher positive values indicate greater preservation within the ZNIEFF.

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Explanatory variables Our choice of explanatory variables reflects the Seine-et-Marne region’s increase in population, which is due to the proximity of Paris. This demographic growth leads to a growing need for housing, resulting in growth in the extent of urban areas. As ZNIEFF is not a protective status, municipalities with large proportions of ZNIEFFs have increased opportunities to urbanize them to meet housing needs, as little non-ZNIEFF land is available. The same is true of municipalities with a large proportion of farmland. We included the following variables: Demographic changes were reflected by the population change in number of inhabitants in a municipality between 1982 and 1999 (Demo). The underlying hypothesis was that more demographic pressure would increase ZNIEFF urbanization. Demographic data were extracted from the INSEE database (National Institute of Statistics and Economic Studies) and census data from 1982 to 1999. The proportion of urban areas in the municipality in 1982 (Urba82) was computed to reflect the urbanization level before ZNIEFF designation (Brotherton, 1996; Li, Zhou, & Ouyang, 2013). Urba82 was a factor that was expected to limit urbanization, as the areas already urbanized in 1982 had less potential for further development. Our hypothesis was that a more urbanized municipality in 1982 would have fewer choices of areas for additional urbanization. The rate (%) of urbanization in the municipality between 1982 and 1999 (Urba_press) was computed as an indicator of urbanization pressure in the municipality for the period of interest. The hypothesis was that municipalities with high urbanization pressure would urbanize ZNIEFF areas to a greater extent. ZNIEFF proportion in the municipality (%ZNIEFF) was computed to reflect ZNIEFF urbanization or preservation. The hypothesis was that a municipality with a large proportion of ZNIEFFs would exhibit an increased risk of urbanizing its ZNIEFFs to meet its needs, as little non-ZNIEFF area was available. The proportion of farmland in the municipality in 1982 (Farm82) was computed as an indicator of the initial reserve of farmland in the municipality (Brotherton, 1996). We have observed that urbanization mainly occurs on farmland in the Seine-et-Marne region (Mimet et al., 2013). The hypothesis was that farmland could have a protective effect in keeping ZNIEFFs from being urbanized; thus, municipalities with large proportions of farmland in 1982 would be less likely to urbanize ZNIEFFs. Slope and hydrology are likely to increase and decrease, respectively, the probability of an area being urbanized and/or being attractive for human settlement. Hydrological conditions (soil flooding) and slope can make urbanization more difficult (Lynch & Liu, 2007; Netusil, 2005). On the other hand, proximity to large bodies of water (such as the Seine and the Marne rivers in Seine-et-Marne) and surrounding low-lying areas can also be attractive because of the view (Mooney & Eisgruber, 2001; Shrestha, York, Boone, & Zhang, 2012) and because, especially around Paris, urbanization follows the main roads built in the large valleys. Slope conditions were reflected by an indicator of the difference in the average slope conditions inside and outside of ZNIEFFs (Slope). Slope was extracted from the digital terrain model of the SRTM (Shuttle Radar Topography Mission). A positive value of Slope indicates that the ZNIEFF is steeper than its surroundings. Hydrological conditions were reflected by an indicator of the difference between the averaged distance to the hydrological network inside and outside the ZNIEFFs (Hyd). Positive values of Hyd indicate shorter distances from the hydrological network within the ZNIEFF than outside the ZNIEFF.

Model selection framework Because we expected that there could be nonlinear relationships between the response variable and the explanatory variables, GAM modeling (a generalized additive model) was used with a Gaussian family from the mgcv package (Wood, 2008) in the R language and environment for statistical computing and graphics (R Development Core Team, 2010). GAM is adapted to highlighting nonlinear links between explanatory variables and urbanization within the ZNIEFFs. We ran the GAM with the cubic regression spline at the default parameter. The initial model was built using all of the explanatory variables. To limit the complexity of the model, we only added interaction terms when they added additional information to the model, that is, when they improved the fit of the model. As interaction terms often introduce multicollinearity into models and can lead to the suppression of simple terms, the addition of interaction terms needed to be justified. The selection of a model containing only interactions that added new information without adding too much multicollinearity was performed by following the next two steps. As multicollinearity can only be tested on linear models, the selection of a model with acceptable multicollinearity values (with variance inflation factor (VIF) values under the typical VIF value of 5) was performed using general linear models (GLM). We built a simple model containing all of the individual terms, and we built six other models containing all the individual terms plus one of the possible interaction terms. We tested the seven models for multicollinearity using the VIF function of the CAR package (Fox & Weisberg, 2010). If a model contained one or more variables with VIF values greater than 5, the variable with the highest VIF value was removed from the model. We then re-tested the model for multicollinearity and removed another variable if necessary, continuing until only VIF values under 5 were obtained. We then tested whether the addition of the interaction term improved the fit of the model, using the GCV (generalized cross validation) score (mgcv package, Wood, 2008) obtained from the seven models run with GAM. In the final model, we only kept interaction terms that provided better GCV scores than the model containing only the individual terms. Finally, we tested the resulting model to determine whether adding a smooth function (allowing a nonlinear relationship between the dependent and explanatory variables) to each explanatory variable improved the explained deviance by more than 0.1% (Cans & Lavergne, 1995). We only kept the smooth function when such an improvement occurred. We then tested the resulting model for multicollinearity. The VIF values were less than 5 for all variables. The selected model was as follows:

ZNIEFF  evolwHyd þ Slope þ sðDemoÞ þ sð%ZNIEFFÞ þsðUrba82Þ þ sðUrba PressÞ þ sð%ZNIEFF : Farm82Þ Smooth terms are represented by “s()”. Interactions are indicated by a colon. To limit the uncertainty from model selection, we used a modelaveraging procedure. Model averaging identified the most important terms of the model and provided good approximations of model coefficients (MuMIn library, Barton, 2010). The model averaging procedure identified the best sub-models in an AICc interval of four under the AICc of the best sub-model. The model averaging procedure computed the importance of each explanatory variable, accounting for the number of times that the variable appeared in the best sub-models and for the weight (based on its AICc) of each of the sub-models.

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The variables with a post-probability greater than 0.5, that is, the variables that were contained in more than 50% of the selected models, were considered important. A post-probability of 0.5 corresponds to a p-value of 0.05 computed for a classic GLM (Viallefont, Raftery, & Richardson, 2001). The coefficients from the model averaging were used to predict values of the dependent variable (ZNIEFF-evol) for the municipalities. We obtained a value reflecting the fit of the model by fitting a linear model (LM) to the observed versus predicted values. Identifying ZNIEFF urbanization and preservation contexts The results of the model averaging were used to identify thresholds in the values of the explanatory variables and to characterize the municipalities with more urbanization within or outside their ZNIEFFs. The thresholds were used to classify the values of the explanatory variables into classes with positive or negative effects. Combining the classes of the important explanatory variables highlighted the important factors in how ZNIEFFs evolved in various urbanization contexts. Results To simplify the presentation of the results, the case in which ZNIEFFs were less urbanized than their surroundings during the study period is termed “ZNIEFF preservation”. The opposite case, in which ZNIEFFs were more urbanized than non-ZNIEFFs, is termed “ZNIEFF urbanization.” A simple Friedman test run on the paired urbanization rates outside (average rate of 4.94%) and inside (average rate of 3.39%) ZNIEFFs revealed a significant difference in urbanization rates inside the ZNIEFFs (chi-squared ¼ 8.0065, p ¼ 0.0046). Of the seven tested explanatory variables, five variables were identified as important (post-probabilities over 0.5) in explaining preservation versus urbanization (Table 1). The proportion of ZNIEFFs and the proportion of urbanization in the municipality in 1982 were not important variables. The model that was obtained by the model-averaging procedure fit the data well, with an (R2 ¼ 0.6). Explanatory variable effects Urbanization pressure (Urba_Press) was an important variable (Fig. 2). An increase of at least 0.3% in urbanization favored ZNIEFF

Table 1 Post-probabilities of the explanatory variables extracted from the model averaging results. Post-probabilities are calculated from the number of times the variable contributes to the best selected models, weighted by the AICs of the models. A value of one signifies that the variable was present in all the best selected models. Grey denotes the important variables with post-probabilities greater than 0.5, which are roughly equivalent to p-values less than 0.05 for a classic generalized model .

Variables

Importance (postprobability)

Hyd

0.87

Slope

0.59

s(Demo)

1

s(%ZNIEFF)

0.41

s(Urba82)

0.4

s(Urba_Press)

1

s(Farm82 :%ZNIEFF)

0.87

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Table 2 Values of the classes of the important variables.

Demo Hyd Slope Urba-Press %ZNIEFF Farm82

Class 

Class w

Class þ

<593 <428 >3.25 e <30 <30

>593 >428 <3.25 <3.12 30e60 30e60

e e e >3.12 >60 >60

preservation. Below 0.3%, no clear response could be observed. ZNIEFF preservation also occurred in the context of strong demographic pressure between 1982 and 1999 (Fig. 2). The threshold occurred at a population growth of 40 inhabitants in the municipality between 1982 and 1999. ZNIEFF preservation was favored in contexts of stronger hydrological pressure inside ZNIEFFs than outside ZNIEFFs and by lower slopes inside ZNIEFFs than outside ZNIEFFs (as in valleys). For the slope and the hydrological variables, the threshold separating urbanization of ZNIEFFs from preservation of ZNIEFFs occurred at 0, that is, when the slope and hydrological conditions were the same inside and outside ZNIEFFs. The interaction term between the proportion of ZNIEFFs (% ZNIEFF) and the proportion of farmland in the municipality in 1982 (Farm82) was an important variable (Fig. 2). ZNIEFF urbanization appeared in two contrasting situations with these two variables. ZNIEFF urbanization occurred in the context of low proportions of both ZNIEFFs (less than 30%) and farmland (less than 30%) in the municipality. ZNIEFF urbanization also occurred in the opposite context, indicated by high proportions of ZNIEFF (more than 60%) and farmland (more than 30%). However, ZNIEFF preservation was favored for areas with more than 30% ZNIEFF and less than 30% farmland in 1982 and was also favored for areas with less than 30% ZNIEFF and more than 30% farmland in 1982. Contexts of urbanization and preservation of ZNIEFFs Using the thresholds detailed above, we partitioned the important variables into classes based on the effect of the variable on ZNIEFF urbanization or preservation. Each explanatory variable was divided into two classes. One class contained values that induced ZNIEFF urbanization, and the other class contained values inducing ZNIEFF preservation. To describe the non-linear response induced by the interactions between the proportion of ZNIEFF and the proportion of farmland in 1982 in the municipality, these two variables were partitioned into three classes (Table 2). The predicted values of the dependent variable (ZNIEFF-evol) were also partitioned into three types (Table 3). Values of less than 1 were used to build a class of municipalities that urbanized more inside ZNIEFFs than outside ZNIEFFs (a ZNIEFF urbanization dynamic). Values between 1 and 1 were used to build a class of municipalities that urbanized their territory homogeneously (an equilibrium dynamic). Values greater than 1 were used to build a class of municipalities that urbanized less inside ZNIEFFs than outside ZNIEFFs (a ZNIEFF preservation dynamic). Among the combinations of classes of explanatory variables found for the three dynamics of ZNIEFF evolution, more combinations existed in the ZNIEFF preservation class than in the ZNIEFF urbanization class. The ZNIEFF urbanization dynamic was found in 41 of the 145 municipalities in the study. These municipalities were characterized by only 13 combinations of explanatory variables among the 56 observed in the study area. Of these 41 municipalities, 36 had the same combination of classes of hydrological conditions (low hydrological pressure in the ZNIEFF), demographic growth (low

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Table 3 Observed combinations of the classes of the important explanatory variables and their contributions to the three outcomes of ZNIEFF urbanization, equilibrium, and preservation. Each line corresponds to a combination of classes of the important explanatory variables (left columns). The signs correspond to the class names, as detailed in Table 2. The right-hand columns give the number of municipalities corresponding to each combination of classes distributed in the three outcomes. The grey variations highlight the lines in which we identified a restricted combination of variables as important for the strategy of ZNIEFF evolution. The combination of variables favoring ZNIEFF urbanization (in dark grey) includes low demographic growth and urbanization pressure, less hydromorphous soil inside ZNIEFFs and medium to large proportions of farmlands in the municipalities. ZNIEFF preservation (in light grey) preferentially occurred in municipalities with low to medium total percentages of ZNIEFF and farmlands. Equilibria (middle grey) preferentially occurred in cases of low demographic growth and low urbanization pressure . Explanatory variables Urba%ZNIEFF Press

Strategy

Demo

Hyd

Slope

Farm82

-

+ + + + + +

+ + + + + + + + + + +

+ + + + + + + + + + -

~ ~ ~ + + ~ ~ + ~ ~ + + ~ + ~ + + -

~ + ~ + ~ ~ ~ ~ + ~ ~ ~ ~ ~ ~ ~ ~

+ + + + + + + + + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + + + + + +

+ + + + + + + + + + + +

~ ~ + + ~ ~ ~ ~ + ~ ~ ~ ~ +

+ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ + -

ZNIEFF urbanization 2 1

Equilibrium 4 1

ZNIEFF preservation 3 1

4

5 1

1 1

2

10 6

7

1

10 1 2 1

1 1 1 1

2 1

1 2

1

3 1 1

3 2

3

1

2 1 1 2

1 1 5 1

1 1 2 3 1

2

1 3 1 3 1 1 1 5 2 1 1 2 1 1 2 1 2 1 1 1

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Fig. 3. Strategies leading to ZNIEFF evolution in Seine-et-Marne and the anthropogenic factors associated with ZNIEFF preservation. a) Predicted location of the three dynamics of ZNIEFF evolution in the Seine-et-Marne region: ZNIEFF preservation is favored in the northwestern portion of the region, near Paris (light grey). ZNIEFF urbanization is favored in the southern and western portions of the region, in the rural areas (dark grey). b) Variability of demographic growth (Demo) and urbanization pressure (Urba-Press) in municipalities where conditions favor ZNIEFF preservation. Near Paris, the municipalities preserving ZNIEFFs mainly have strong demographic growth and urbanization pressure. Farther from Paris, ZNIEFF preservation may also occur in municipalities with low urbanization pressure and low demographic growth rates. The municipalities represented in white do not preserve ZNIEFFs.

values), urbanization pressure (low urbanization values) and proportion of farmland in 1982 (over 30%). The ZNIEFFs preservation dynamic was found in 61 of the 145 municipalities in the study. These municipalities were characterized by many combinations of explanatory variables (40 of the 56 combinations observed in the study area) and by their proportions of ZNIEFF and farmland in 1982 following the response to the interaction term. In this class, 87% of the municipalities had i) an intermediate to high proportion of ZNIEFF and a low proportion of farmlands, ii) an intermediate proportion of both ZNIEFF and farmland or iii) a high proportion of farmland and a low to intermediate proportion of ZNIEFF. High demographic growth and urbanization pressure did not truly characterize this class, as these factors were not important for a majority of these municipalities, although they were represented. Mapping the municipalities with respect to their dynamics (Fig. 3) revealed spatial patterns. The municipalities that preserved ZNIEFFs were located near Paris, in the northwest part of the study area. The municipalities that urbanized ZNIEFFs more were located in rural areas in the eastern part of the study area. Near Paris, ZNIEFF preservation appeared to be mainly due to strong urbanization pressure and demographic growth. Farther from Paris, ZNIEFF preservation appeared to occur in contexts of low demographic and urbanization pressures. Discussion The results of this study confirm the notion that the simple designation of a natural area (“ZNIEFF” in France) may influence the development of these areas. That is, knowledge of the value of a natural area may influence the choices of land use planners and

therefore influence the area’s fate (Mathevet & Lepart, 2013). Our results suggest that designation alone may in some cases have a negative effect on the evolution of natural areas. This work provides information about the direction of the socio-economic pressures on protected natural areas in suburban areas. As suggested by Mathevet and Lepart (2013), the evolution of the ZNIEFF, without any legal constraint, could be interpreted as an indicator of humanenature relationships and the effects of human actions on natural areas. From this point of view, ZNIEFF evolution addresses the question of how knowledge of designated natural areas is used by land use planners (Mathevet & Lepart, 2013). Our results identify geographic conditions that favor preservation of designated natural areas in an anthropogenic context, as well as the conditions favoring urbanization. They also suggest that the local scale (municipalities in France) is important to the future and evolution of designated areas with respect to urbanization. Accounting for administrative and decision-making boundaries (here, municipality boundaries) in the analysis of the evolution of natural areas (here, ZNIEFFs) appears to be important (Bürgi et al., 2004), and it yields some information on the weight of local policies. Conditions for ZNIEFF preservation The conditions that favor ZNIEFF preservation vary much more than the conditions that favor ZNIEFF urbanization. This variation shows that preservation is the most common dynamic that has emerged in response to legal ZNIEFF designation, while urbanization arises from specific conditions. However, a condition needs to be met to allow preservation; this condition seems to be linked to the relative proportions of ZNIEFF and farmland in the municipality. In our study, there was no preservation when the proportions of

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both ZNIEFF and farmland were low or high. In other words, preservation depends on an intermediate proportion of both ZNIEFF and farmland. In terms of management and nature protection, this result seems to indicate that preserving nature is easier and may represent less of a constraint for management in municipalities with diversified activities, where neither urbanized area nor farmland nor ZNIEFF is dominant. The proportion of farmlands was expected to be a reserve for urbanization and act as a shield against ZNIEFF urbanization. We found that this shield effect exists, but only in cases where farmland accounts for more than 30% of the municipality. Under this threshold, the shield effect does not exist, highlighting a new relationship between farmland and ZNIEFF preservation. When a municipality has less than 30% farmland, the importance of preserving farmland increases and exceeds the importance of preserving ZNIEFFs in land use planning. Other factors linked to the human development of municipalities are important in ZNIEFF preservation. In our case study, these factors are expressed by the importance of demographic growth and urbanization pressure in municipalities between 1982 and 1999. This observation might be explained by the fact that designated areas are better taken into account in highly urbanized areas where urbanization is carefully planned to meet strong social needs. Nevertheless, this responsedpreserving designated areas in urban contextsdremains counterintuitive when such regions have a strong need to urbanize. Thus, it seems that natural areas are especially important in these urban contexts. One of the most easily documented explanations can be found by examining the amenities provided by natural areas, which strongly increase in urban contexts and which could justify a positive attitude toward natural areas in these urban contexts (Tyrvainen, 2000). These results are comparable to those of Brotherton (1996), who showed that in Europe, the population density and the degree of urbanization of a country increased the demand for protection and consequently the area protected. Importantly, the dependent variable in this study, the indicator of ZNIEFF evolution, is a continuous variable that reflects the difference in changes in urbanization between areas inside and outside ZNIEFFs. This variable does not, however, reflect complete urbanization versus protection of ZNIEFFs. Thus, even if municipalities under strong human pressure and located near Paris clearly urbanize their ZNIEFFs less than non-ZNIEFFs, it does not mean that they do not urbanize them at all. In this context, the gross proportion of ZNIEFF urbanization in urban municipalities could exceed the gross proportion of urbanization of ZNIEFFs in rural municipalities. The consequences, in terms of effective preservation and conservation, do not necessarily follow the results and trends highlighted by this study. However, the method used in this study achieves the objective of identifying the contexts associated with preservation of designated natural areas. Conditions for ZNIEFF urbanization While the conditions linked to the preservation of designated natural areas vary, the conditions leading to their urbanization are much clearer and are correlated with the co-occurrence of four characteristics. ZNIEFF urbanization is more likely to occur in municipalities with little human development, represented in this study by negative or small demographic growth and little initial urbanization. ZNIEFF urbanization also corresponds with ZNIEFFs that are, on average, more distant from hydrological networks, indicating that hydromorphy limits urbanization (Lynch & Liu, 2007; Netusil, 2005). Finally, these municipalities contain moderate to high proportions of farmland. The municipalities that manifest all of these conditions are rural municipalities with a focus on agricultural activities. Our results are

consistent with those of previous studies that have shown that socioeconomic conditions must be considered when delineating protected areas because they affect the beneficial effect (Buisson & Dutoit, 2006; Mathevet & Tamisier, 2002). Our results at the regional scale could be compared with results of studies at national and global scales. At these different scales, biodiversity loss due to land cover change is greater in less-developed areas (Mikkelson, Gonzalez, & Peterson, 2007), as in the east of the Seine-et-Marne region, while richer regions have more protected areas (McDonald & Boucher, 2011). The fact that the urbanization of designated areas is stronger in municipalities with low human development pressure is not obvious and could be counterintuitive. Indeed, preservation should be easier if there is little urbanization pressure. Thus, this result indicates that the need for urbanization is not the main factor in this rural context and that other underlying factors exist. One such factor may be development, which could explain this result in two ways. First, in an agricultural municipality, farmlands are important and must be preserved to maintain agriculture and the local economy. Second, natural areas are recognized as being attractive for human settlement because of the amenities that they provide (Tyrvainen, 2000). The attractiveness of natural areas could increase the price not only of the natural land (Irwin, 2002) but also of the land around natural areas (Jim & Chen, 2009; Thorsnes, 2002). Thus, ZNIEFF urbanization could be used in rural contexts as a way to increase the economic and amenity value of natural areas. The last explanation is independent of the development hypothesis. This explanation rests on the idea that in rural areas, the patrimonial aspect of natural areas is less important because the countryside resembles natural areas, is largely available for everybody, is present everywhere and may have the same amenities as natural areas (Jordan & Warner, 2010). Therefore, the countryside can supply some of the functions and human wellbeing amenities provided by natural areas, reducing the relative interest in natural areas. Preservation but isolation in an urban context Beyond the evolution of the designated area itself, the results of this study also raise the question of the consequences of the observed urbanization just beyond the limits of the designated areas. They also raise the question of whether designated areas necessarily become isolated. Human activities just outside protected areas have been observed all over the world, whether the dominant human activity was urbanization, deforestation or agriculture, as well as at various scales of protected areas (Clerici et al., 2007; DeFries, Hansen, Newton, & Hansen, 2005; Svancara, Scott, Loveland, & Pidgorna, 2009). Observed at the municipality scale, ZNIEFF preservation necessarily signifies an increase in urbanization outside ZNIEFFs but within the municipality, to address social needs for urbanization. The results of the present study show that the risk of transfer of urbanization from designated natural areas to undesignated areas located just outside the designated areas in municipalities may increase the isolation risk for designated areas. Such a mitigating effect can explain the absence of significant result found by Borgstrom, Cousins, and Lindborg (2012) at the city scale. Thus, our results highlight the importance of political gridding, the unit of land management on which human pressure and needs take place, and of the intersection of this gridding with the delineation of protected/designated areas (Wade & Theobald, 2009). The proportion of designated areas is important for the evolution of the designated area, as is the proportion of the land cover acting as a reserve for the dominant human activity (here, farmlands as a reserve for urbanization). This spatial intersection also has consequences for the transfer of the activity outside of designated areas

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and for increases in isolation, and it must be considered when designing areas and more generally protected areas. The intersection of political gridding and the delineation of natural areas should allow enough flexibility for the municipality to address both social and development needs. Potential implications for natural area management and the limitations of this study The results of this study could have two implications for biodiversity preservation in highly anthropogenic contexts. First, designation is a preservation tool that could be effective in some social and anthropogenic contexts and deserves to be broadly tested. The advantages of this tool are its flexibility for local managers and its limited cost. Our study was restricted to a unique suburban region with various urbanization and development situations. However, this region is not representative of the human and natural contexts of all French regions and even less of all other regions worldwide. Comparisons with other regions could reveal general and contrasting patterns. Moreover, research into the socioeconomic processes leading to ZNIEFF preservation/urbanization in municipalities would contribute to a better understanding of the human causes and issues leading to ZNIEFF evolution. In our study, designation appeared to be of particular interest in highly dynamic areas where designated areas were more preserved over time. The fact that the strong legal frameworks employed by common protection tools may sometimes be questioned by landowners in these particular areas (Abakerli, 2001) may also reinforce the interest in designation in these areas. Thus, it would be of interest to investigate whether designation would allow an enlargement of preserved natural areas in suburban contexts. Second, our study was limited to studying the differences in urbanization rates inside and outside ZNIEFFs and thus does not produce an answer to the paramount question of the consequences of designation on real (and not relative) preservation of the designated areas. Here again, more investigation is needed to explore the effects of ZNIEFF designation on biodiversity conservation. Moreover, the law concerning ZNIEFFs has evolved considerably since the end of the period considered in our study. More specifically, local government agencies (here, municipalities) have been responsible for the protection of natural areas since 2000. Given that local government agencies cannot ignore ZNIEFFs, the ZNIEFF designation may now be a stronger protection tool. It thus would be of interest to explore how ZNIEFF designation has influenced the evolution of natural areas since 2000. Acknowledgments The authors wish to thank the IAU (Institut d’Aménagement et d’Urbanisme) for the land use and cover data. We also wish to thank the anonymous reviewers for their careful review of this paper, which significantly improved its quality. This study was funded by the program “Atlas dynamique de la biodiversité en Seine-etMarne” of the Seine-et-Marne region. References Abakerli, S. (2001). A critique of development and conservation policies in environmentally sensitive regions in Brazil. Geoforum, 32, 551e565. Barton, K. (2010). MuMIn: Multi-model inference. In R package version 0.13.14. Borgstrom, S., Cousins, S. A. O., & Lindborg, R. (2012). Land use changes in the surroundings of urban nature reserves. Applied Geography, 32, 350e359. Brotherton, I. (1996). Protected area theory at the system level. Journal of Environmental Management, 47, 369e379. Bruner, A. G., Gullison, R. E., Rice, R. E., & da Fonseca Gustavo, A. B. (2001). Effectiveness of parks in protecting tropical biodiversity. Science, 291, 125e128.

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Buisson, E., & Dutoit, T. (2006). Creation of the natural reserve of La Crau: implications for the creation and management of protected areas. Journal of Environmental Management, 80, 318e326. Bürgi, M., Hersperger, A. M., & Schneeberger, N. (2004). Driving forces of landscape change e current and new directions. Landscape Ecology, 19, 857e868. Burton, R. J. F., & Paragahawewa, U. H. (2011). Creating culturally sustainable agrienvironmental schemes. Journal of Rural Studies, 27(1), 95e104. http:// dx.doi.org/10.1016/j.jrurstud.2010.11.001. Cans, C., & Lavergne, C. (1995). De la régression logistique vers un modèle additif généralisé: un exemple d’application. Revue de statistique appliquée, 43, 77e90. Clap, F. (2005). Le juge et les ZNIEFF. NATUR-AE. Clerici, N., Bodini, A., Eva, H., Grégoire, J.-M., Dulieuc, D., & Paolini, C. (2007). Increased isolation of two biosphere reserves and surrounding protected areas (WAP ecological complex, West Africa). Journal of Nature Conservation, 15, 26e 40. Couderchet, L., & Amelot, X. (2010). Faut-il brûler les ZNIEFF. Cybergeo: European Journal of Geography. http://dx.doi.org/10.4000/cybergeo.23052. Crumpacker, D. M. (1998). Prospects for sustainability of biodiversity based on conservation biology and US Forest Service approaches to ecosystem management. Landscape and Urban Planning, 40, 47e71. DeFries, R., Hansen, A., Newton, A. C., & Hansen, M. C. (2005). Increasing isolation of protected areas in tropical forests over the past twenty years. Ecological Applications, 15, 19e26. Devictor, V., Godet, L., Julliard, R., Couvet, D., & Jiguet, F. (2007). Can common species benefit from protected areas? Biological Conservation, 139, 29e36. Downsborough, L., Shacketon, C. M., & Knight, A. T. (2011). The potential for voluntary instruments to achieve conservation planning goals: the case of conservancies in South Africa. Oryx, 45(3), 357e364. http://dx.doi.org/10.1017/ S0030605310001559. Elissalde-Videment, L., Horellou, A., Humbert, G., & Moret, J. (2004). Guide méthodologique sur la modernisation de l’inventaire des zones naturelles d’intérêt écologique, faunistique et floristique, Mise à jour 2004. Paris: Muséum National d’Histoire Naturelle. Fox, J., & Weisberg, S. (2010). Car: Companion to Applied Regression. R package version 2.0-2. http://CRAN.R-project.org/package¼car. Haberl, H., Gaube, V., Díaz-delgado, R., Krauze, K., Neuner, A., Peterseil, J., et al. (2008). Towards an integrated model of socioeconomic biodiversity drivers, pressures and impacts. A feasibility study based on three European long-term socio-ecological research platforms. Ecological Economics, 68(6), 1797e1812. http://dx.doi.org/10.1016/j.ecolecon.2008.11.013. Hage, M., Leroy, P., & Petersen, A. (2012). Stakeholder participation in environmental knowledge production. Future, 42, 254e264. INSEE. (2013). http://insee.fr/fr/. Irwin, E. G. (2002). The effects of open space on residential property values. Land Economics, 78, 465e480. IUCN. (2010). http://www.uicn.fr/Les-espaces-proteges-francais-un.html. Jim, C. Y., & Chen, W. Y. (2009). Landscape and Urban Planning Value of scenic views: hedonic assessment of private housing in Hong Kong. Landscape and Urban Planning, 91, 226e234. http://dx.doi.org/10.1016/j.landurbplan.2009.01.009. Jordan, N., & Warner, K. D. (2010). Enhancing the multifunctionality of US agriculture. Bioscience, 60, 60e66. Kates, R. W., Clark, W. C., Corell, R., Hall, J. M., Jaeger, C. C., Lowe, I., et al. (2001). Environment and development: sustainability science. Science, 292, 641e642. Khan, M. S., & Bhagwat, S. A. (2010). Protected areas: a resource or constraint for local people? Mountain Research and Development, 30(1), 14e24. Lefebvre, T. M. S., & (coordination). (2010). Les espaces protégés français : une pluralité d’outils au service de la conservation de la biodiversité (pp. 98) (Paris, France). Li, X., Zhou, W., & Ouyang, Z. (2013). Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38, 1e10. Locke, H., & Dearden, P. (2005). Rethinking protected area categories and the new paradigm. Environmental Conservation, 32, 1e10. Lynch, L., & Liu, X. (2007). Impact of designated preservation areas on rate of preservation and rate of conversion: preliminary evidence. American Journal of Agricultural Economics, 89, 1205e1210. Mathevet, R., & Lepart, J. (2013). Développement durable et territoires. Développement Durable et Territoires, 4(Avril), 1e16. Mathevet, R., & Tamisier, A. (2002). Creation of a nature reserve, its effects on hunting management and waterfowl distribution in the Camargue (southern France). Biodiversity and Conservation, 11, 509e519. McDonald, R. I., & Boucher, T. M. (2011). Global development and the future of the protected area strategy. Conservation Biology, 144, 383e392. McDonald, R. I., Kareiv, P., & Forman, R. T. T. (2008). The implications of current and future urbanization for global protected areas and biodiversity conservation. Biological Conservation, 141, 1695e1703. Mckinney, M. L. (2005). Urbanization as a major cause of biotic homogenization. Biological Conservation,127, 247e260. http://dx.doi.org/10.1016/j.biocon.2005.09.005. Mikkelson, G. M., Gonzalez, A., & Peterson, G. D. (2007). Economic inequality predicts biodiversity loss. PLoS One, 2, e444. doi: 10.1371. Millennium Ecosystem Assessment. (2005). Millennium ecosystem assessment. Ecosystems and human well-being. In Biodiversity synthesis. W. Island Press D.C.. http://www.millenniumassessment.org/documents/document.354.aspx. pdf (Assessed December 2012), Ed.

352

A. Mimet et al. / Applied Geography 45 (2013) 342e352

Mimet, A., Houet, T., Julliard, R., & Simon, L. (2013). Assessing functional connectivity: a landscape approach for handling multiple ecological requirements. Methods in Ecology and Evolution, 4, 453e463. http://dx.doi.org/10.1111/2041210x.12024. Mooney, S., & Eisgruber, L. M. (2001). The influence of Riparian protection measures on residential property values: the case of the Oregon Plan for salmon and watersheds. The Journal of Real Estate Finance and Economics, 22, 273e286. Netusil, N. R. (2005). The effect of environmental zoning and amenities on property values: Portland, Oregon. Land Economics, 81, 227e246. Pfaff, A., Robalino, J., Sanchez-azofeifa, G. A., & Andam, K. S. (2009). The B. E. Journal of economic analysis & policy contributions park location affects forest protection: land characteristics cause differences in park impacts across Costa Rica Park location affects forest protection: land characteristics cause differe. Economic Analysis, 9(2). R Development Core Team. (2010). R: A language and environment for statistical computing, ISBN 3-900051-07-0. URL http://www.Reproject.org. Rosenzweig, M. L. (2003). Reconciliation ecology and the future of species diversity. Oryx, 37, 194e205. Sanchez-Azofeifa, A. G., Daily, G. C., Pfaff, A. S. P., Sa, G. A., & Busch, C. (2003). Integrity and isolation of Costa Rica’ s national parks and biological reserves: examining the dynamics of land-cover change. Biological Conservation, 109, 123e135. Shrestha, M. K., York, A. M., Boone, C. G., & Zhang, S. (2012). Land fragmentation due to rapid urbanization in the Phoenix Metropolitan Area: analyzing the spatiotemporal patterns and drivers. Applied Geography, 32, 522e531.

Simpson, R. D., & Sedjo, R. A. (1996). Paying for the conservation of endangered ecosystems: a comparison of direct and indirect approaches. Environment and Development Economics, 1, 241e257. Streck, C. (2004). New partnerships in global environmental policy: the clean development mechanism. The Journal of Environment and Development, 13, 295e 322. Svancara, L. K., Scott, J. M., Loveland, T. R., & Pidgorna, A. B. (2009). Remote sensing of environment assessing the landscape context and conversion risk of protected areas using satellite data products. Remote Sensing of Environment, 113(7), 1357e1369. http://dx.doi.org/10.1016/j.rse.2008.11.015. Thorsnes, P. (2002). The value of a suburban forest preserve: estimates from sales of vacant residential building lots. Land Economics, 78, 426e441. Tyrvainen, L. (2000). Property prices and urban forest amenities. Journal of Environmental Economics and Management, 39, 205e223. United Nations Environment Programme International Union for the Conservation of Biodiversity and the Convention on Biodiversity. (2006). Developing international payments for ecosystem services: a technical discussion (Geneva, Switzerland). Viallefont, V., Raftery, A. E., & Richardson, S. (2001). Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine, 20, 3215e3230. Wade, A. A., & Theobald, D. M. (2009). Residential development encroachment on U.S. protected Areas. Conservation Biology, 24(1), 151e161. Wood, S. N. (2008). Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society, 70, 495e518.