The Effectiveness of Municipal Comprehensive Planning in Mitigating Forest Fragmentation

Abstract

To mitigate forest fragmentation, it is critical to investigate the factors that contribute to it. Considerable research examines various factors shaping forest fragmentation, but these studies have not typically identified land use planning as an important factor. Our study helps fill this gap in the literature. To examine the influence of land use planning on forest fragmentation, we assessed the quality of 29 municipal comprehensive plans with respect to forest protection in Wisconsin, U.S.A., a sample drawn from a spatial analysis of forest fragmentation dynamics from 2001 to 2011. We found that the comprehensive plans of communities that have experienced lower-than-expected forest fragmentation change were, on average, of significantly higher quality than those of the communities that have experienced higher-than-expected forest fragmentation change during this period. We conclude that the quality of community-based comprehensive plans, especially the strength and breadth of policies addressing forestland, matters in mitigating the fragmentation of forestland.

1. Introduction

Forests are essential for the health of our planet. They provide massive benefits in response to the threats stemming from changing climate, not only because they sequester large amounts of carbon but also because they provide a cooling effect. According to the International Panel on Climate Change, “reducing deforestation and forest degradation rates represents one of the most effective and robust options for climate change mitigation, with large mitigation benefits globally” [1]. Moreover, forests provide habitat for roughly 80% of all wildlife species that live on land [2], bring psychological and physiological benefits to individuals [3], and deliver many other benefits to communities and individuals, including increased recreational opportunities and property values, mitigation of stormwater runoff, and economic gains from forestry.

According to National Geographic [4], forests are disappearing at an alarming rate: 502,000 square miles of forests, an area larger than South Africa, were lost globally between 1990 and 2016. More recently, wildfires have raised concern, including the 2019 Amazon rainforest wildfires that resulted in the loss of roughly 3500 square miles of forests in the world’s largest carbon sink, the 2019/2020 Australian wildfires that claimed roughly 16,600 square miles of forests, and the 2023 Canadian wildfires that devastated roughly 71,000 square miles of forests. In addition to wildfires, forestry-based and agricultural uses, climate stress, infestations, and urbanization all contribute to significant losses and changes in the makeup of forestland.

Even when the total area of forestland remains stable, the benefits of forests are reduced when the forestland is fragmented. Fragmentation occurs when large contiguous forestlands break apart into smaller pieces through natural factors and human activities alike. It causes loss of wildlife habitat and biodiversity [5,6,7,8,9], alters ecosystem functions [10,11,12,13,14], and negatively impacts the timber industry and forestry [15]. In a nutshell, forest fragmentation poses serious threats to ecosystems and communities [16,17]. If we are to mitigate forest fragmentation, we must understand its extent and causes. Numerous physical, social, and political factors shape forest fragmentation (see, for instance, [18,19,20,21,22]). Among these factors is land use planning.

Land use planning identifies how and where communities will grow, and hence can play a significant role in protecting natural and working lands. Despite its potential significance, however, there is limited evidence on how land use planning mitigates or facilitates forest fragmentation [23].

With this paper, we contribute to the limited literature on the influence of land use planning on the mitigation of forest fragmentation and the creation of environmentally sustainable communities. More specifically, we investigate the influence of community-based comprehensive plans on the mitigation of forest fragmentation in the state of Wisconsin. To the best of our knowledge, our study is the only one that examines the effectiveness of the practice of municipality-based comprehensive planning with respect to forest protection in mitigating forest fragmentation. We hypothesize that plan quality matters: low-quality plans in terms of forest protection (hereafter called low-quality plans) may facilitate forest fragmentation in a community, whereas high-quality plans in terms of forest protection (hereafter called high-quality plans) may mitigate forest fragmentation.

1.1. The Effectiveness of Land Use Planning Practices, Policies, and Regulations in Natural Resource Protection

There is some empirical evidence suggesting that land use planning–related practices, regulations, and policies have succeeded in protecting natural resources (see, for instance, [23,24,25]). Examining different planning schemes, Liu et al. [23] showed the importance of targeted natural protection policies: in urban zones in the Ningbo region of China, the land use planning policy “The Ecological City Policy”, which had the most significant emphasis on forest protection among other regional policies, had positive and the strongest association with mitigation of forest fragmentation. However, the evidence of the effectiveness of land use planning-related practices, regulations, and policies in natural resource protection is mixed. For instance, urban growth boundaries (UGB), which are created largely to protect farmland and natural resources, have helped to concentrate land development within their boundaries and reduce development on forest and farmlands in Oregon, the state that pioneered the implementation of UGBs [26,27,28]. However, other studies [29,30] found that Oregon’s policy of growth management through UGBs directed new development in a leapfrog fashion into neighboring communities in the state of Washington, with negative consequences for forests and other resources elsewhere. Similarly, there is mixed evidence of other states’ policies and regulations. Maryland is a case in point. Shen and Zhang [31] found that the state’s Smart Growth Program increased the probability of land development within Priority Funding Areas (i.e., areas that are intended to concentrate development) and decreased development within Rural Legacy Areas (i.e., areas designated to protect natural and resource lands). However, Jantz et al. [32] estimated that the urban fringe would continue to be developed in a low-density pattern and consume forests and other natural resources, as land outside the Priority Funding Areas had minimal protection under current policies.

In addition to producing mixed findings, empirical work on the evaluation of regional and statewide land use policies and programs is limited in quantity [23,33]. This is understandable when statewide efforts are rather recent, and it is too early to assess outcomes. For example, it has been only 24 years since the State of Wisconsin passed its Comprehensive Planning Law [34], also known as the Smart Growth Law, which places a significant emphasis on the protection of natural resources.

Furthermore, when land use planning is incorporated in the statistical analysis of forest fragmentation, it has been treated as a dummy variable that indicates, for example, the presence or absence of a master land use plan (see, for instance, [25]). However, land use plans can differ substantially in their quality. For instance, clearly identified goals, timelines, responsible parties, and funding sources can make a substantial difference in environmental protection outcomes. To determine the impact of land use planning on natural resources, it is important to evaluate the relationship between the quality (not the mere existence) of community-based plans and the state of natural resources.

1.2. The Study Area and Scope

In this research, we investigate the effectiveness of Wisconsin’s municipal comprehensive plans for forest protection. More specifically, we investigated the relationship between comprehensive plan quality and forest fragmentation dynamics. To the best of our knowledge, only one other study [33] has evaluated Wisconsin’s Comprehensive Planning Law. Yet Edwards and Haines [33] investigated not the outcomes of the plans but rather how well community plans produced under this law promoted smart growth principles. Studying the effects of comprehensive plans is particularly important for Wisconsin’s communities, which are updating or preparing plans following the guidelines and requirements contained in the law. It is also important to study the effects of comprehensive planning on forests because Wisconsin is blessed with a variety of high-quality natural resources, including forests. Forestland covers roughly half (49%) of the state land area, about two-thirds of which are located in northern Wisconsin.

Although the amount of forestland in Wisconsin has not decreased in recent decades, the spatial composition and configuration of that forestland have undergone many changes that have resulted in significant forest fragmentation throughout the state. Much of the forest fragmentation results from extensive road construction across the state and the development of seasonal and retirement housing in northern areas [19,20]. The following statistics suggest the extent of seasonal and retirement housing development in northern Wisconsin. While the population increased by only 6% between 1940 and 1990, the number of housing units increased by 113% during the same period [20]. As a result, forest fragmentation has accelerated significantly, especially in suburban and rural areas that are rich in natural amenities [35,36]. Furthermore, dividing forest property into smaller properties through parcelization, a trend observed in Wisconsin [37], makes forestland more susceptible to fragmentation because smaller land tracts are easier to sell for development [38].

1.3. Wisconsin’s Comprehensive Planning Law

In Planning for Smart Growth, the American Planning Association (APA) categorized Wisconsin among the states implementing moderate to substantial statewide reforms [39]. However, Wisconsin’s Comprehensive Planning Law did not mandate growth management, adoption of a comprehensive plan, or the promotion of smart growth in comprehensive plans. It did, however, mandate comprehensive plan adoption by 2010, with updates every 10 years or sooner, if a county or municipality wanted to administer zoning, subdivision, and official map ordinances. Comprehensive plans must have nine elements that address a number of planning domains (such as housing, transportation, and land use) and planning processes (such as implementation and identification of issues and opportunities).

Forest fragmentation in Wisconsin has been recognized as a critical issue and a growing concern. For instance, one of the Comprehensive Planning Element Guides, “Planning for Natural Resources: A Guide to Including Natural Resources in Local Comprehensive Planning”, states: “Differing views over the use of forest resources have led to conflicts over the proper uses of forests. Increased recreational land ownership in parts of the state has caused the fragmentation of forests resulting in reduced wildlife habitat and ecosystem integrity. Planning can help strike a balance between competing forest use issues” ([40], p. 51). Two of the nine required elements (i.e., land use and agricultural, natural, and cultural resources) can have a potentially critical influence on forest fragmentation.

2. Materials and Methods

2.1. Sample Selection

To determine the effect of plan quality on forest fragmentation, we examined a sample of municipal plans. Sampling was necessary due to the sheer number of comprehensive plans. For the time period we studied, 1402 of the 1851 Wisconsin municipalities had adopted a comprehensive plan [41], making it infeasible to evaluate all plans. Our sample was determined by a prior statewide analysis of forest fragmentation dynamics in which we examined forest fragmentation changes from 2001–2011 at the municipal level [42]. We chose this period because it roughly captures the initial implementation of comprehensive planning in Wisconsin (the law passed in 1999, and the deadline for completing mandatory comprehensive plans was 2010). This period also aligns with the availability of the National Land Cover Database (NLCD), which we used to analyze forest fragmentation.

To analyze forest fragmentation, we chose landscape ecology–based metrics that capture different aspects of landscape composition and configuration. Guided by the literature, our selected metrics were mean patch size (MPS), largest patch index (LPI), edge density (ED), and interspersion and juxtaposition index (IJI). (Each of these metrics measures a certain aspect of fragmentation and reveals particular information about ecological processes. MPS is the average size of all patches of interest (i.e., forestland) in a landscape. LPI is the percentage of total landscape area covered by the largest patch of the land cover class of interest. ED is measured as the total edge length of the land cover class of interest in a landscape divided by the total area of the landscape. IJI examines the distribution of the land cover of interest with respect to all other land cover classes. It is calculated as the observed interspersion divided by the maximum interspersion for the total number of land cover classes in the landscape. Low IJI values indicate that land cover classes are distributed disproportionally. High IJI values, which imply that different land cover classes are evenly interspersed in a “salt and pepper” mixture, typically indicate fragmentation.) While there are exceptions, small mean patch size, small largest patch index, high edge density, and high interspersion and juxtaposition index are typically associated with fragmented landscapes.

Our sample of municipal plans was based on the findings of the forest fragmentation analysis, capturing relative changes in the four metrics we calculated on forestland between 2001 and 2011. More specifically, we selected a sample of plans based on a set of regression analyses that modeled forest fragmentation dynamics for the selected metrics. (The regression analyses examined the influence of a number of socioeconomic, geophysical, proximity, community, and policy factors. Guided by the literature on forest fragmentation, we used the following municipal-level independent variables in the regression analysis: the socioeconomic factors of population and population change, household income, and agricultural and forest land price; the geophysical and amenity factors of slope, public lands, and hydric soils; the proximity factors of proximity to urban center, primary roads, and large lakes; the community-characteristics factors of municipal land area, percentage of water, urban land, and agricultural land; and the policy factors of the adoption of a comprehensive plan and the presence of zoning ordinances.) In undertaking the regression analysis, we hypothesized that the quality of a municipal comprehensive plan was the only significant factor missing from the statewide regression models of forest fragmentation change over time.

We expected that the quality of the plan significantly contributed to the unexpected fragmentation change rate (i.e., fragmentation dynamics) findings in the regression models, and we hypothesized that including a plan quality variable could help further explain forest fragmentation dynamics and improve model performance. If the model significantly overestimated forest fragmentation change (that is, the actual forest fragmentation change rate was significantly lower than the model estimates), we hypothesized that the municipality’s comprehensive plan was of high quality and effective in mitigating forest fragmentation. In contrast, if the model significantly underestimated forest fragmentation change (that is, the actual forest fragmentation change rate was significantly higher than the model estimates), we hypothesized that the municipality’s comprehensive plan was of low quality and that it facilitated forest fragmentation. (Significant overestimation or underestimation is determined by standardized residuals with an absolute value greater than 2, which is a commonly used cutoff value for outliers (e.g., see [43]). The results from the statewide regression analysis showed that two out of the four forest fragmentation measures (edge density and mean patch size) have a relatively good model fit, with R² values of 0.285 and 0.123, respectively. However, because only two plans (Village of West Baraboo and Town of Bayfield) had absolute standardized residuals greater than 2 for these two metrics, we based our sample selection on the standardized residuals of edge density alone).

Our sampling strategy resulted in the identification of 14 municipalities with a lower-than-expected forest fragmentation change rate (Group I) and 15 municipalities with a higher-than-expected forest fragmentation change rate (Group II) among Wisconsin municipalities. These 29 municipalities are spread throughout the state in 24 of Wisconsin’s 72 counties. Cities constituted a higher proportion (8 out of 14) of the municipalities with a lower-than-expected forest fragmentation change rate (hereafter, low forest fragmentation dynamics or low FFD). On the other hand, villages and unincorporated towns constituted a higher proportion (9 out of 15) of the municipalities with a higher-than-expected forest fragmentation change rate (hereafter, high forest fragmentation dynamics or high FFD; Table 1).

2.2. Evaluation Protocol for Comprehensive Plan Quality

Our evaluation protocol for comprehensive plan quality (Table 2) is based primarily on a comprehensive framework for local plan quality developed by Berke and Godschalk [44]. Driven by empirical studies on plan quality evaluation, Berke and Godschalk’s framework considers 10 criteria in the internal and external dimensions of plan quality. Using Berke and Godschalk’s detailed framework for each criterion and Edwards and Haines’ [33] more complete framework for evaluating policy criteria, we developed specific criteria related to forestland protection and forest fragmentation (Table 2).

We assigned double weight to three criteria—fact base, goals, and policies—when calculating the total score of plan quality, since the literature identifies a strong fact base, clearly articulated goals, and appropriately directed policies as key characteristics of plan quality [45,46]. We measured each criterion on a 0 to 2 ordinal scale: “0” indicates that the criterion is absent from the plan, “1” indicates that the criterion is suggested or identified but not detailed, action-oriented, or expressed in strong language, and “2” indicates that the criterion is fully detailed, action-oriented, or expressed in accompanied by strong language (e.g., timelines and responsible parties provided, use of “must” versus “may”). We then calculated the total score of a plan, summing up the score for each criterion with double scores from fact base, goals, and policies, as described above.

2.3. Evaluation Procedure

To improve the reliability and consistency of the plan quality evaluation results (see [47,48], two coders independently assessed the quality of the sampled municipal comprehensive plans for forest fragmentation and protection. Before we started the evaluation, we randomly chose several Wisconsin comprehensive plans that were not part of the sample to assess these plans and discuss any differences in assessment. We then evaluated the 29 comprehensive plans in our sample. Our inter-reliability score for these 29 plans was 82%, a scientifically acceptable score [49].

2.4. Expectations

We expected to find significant differences between the two groups of plans, such that the plans of Group I (the low-FFD communities) would have significantly higher scores than the plans of Group II (the high-FFD communities).

2.5. Plan Quality Score Analysis

Because plan quality scores are ordinal scale data, we conducted a Mann–Whitney U test to compare the similarity of the two groups’ plan quality scores.

2.6. Excluding Two Cases from Our Analysis

We eliminated two municipalities from our analysis. The first municipality, the City of La Crosse, scored 87 points for its comprehensive plan, 20 points higher than the second highest score in the entire sample. While La Crosse’s comprehensive plan provided substantial opportunities to learn about forest fragmentation mitigation and comprehensive planning, especially in terms of policies, we needed to exclude it from further analysis because it was a clear outlier. The second municipality, the Town of Nokomis, submitted a plan as a comprehensive plan, but we deemed it incomplete, and the Wisconsin Department of Administration confirmed this conclusion. We, therefore, excluded it from further analysis.

3. Results

Our findings show that there is great variability in the quality of plans for addressing forest protection. When we excluded the cases mentioned above, the total scores of the comprehensive plans we examined ranged from 19 (Village of Centuria) to 67 (City of Pittsville). Most often, the plans included goals for the different criteria but lacked concrete and strong actions or language. For example, a plan might state the goal “preserve forests” without providing specific metrics or implementation mechanisms.

3.1. Examining Plan Quality Based on Forest Fragmentation Dynamics

The results in Table 3 show that the low-FFD group had a higher average total score of 50.85, whereas the high-FFD group had an average total score of 41.23. According to the Mann–Whitney U test, the difference between the groups’ total scores was significant at the 0.1 level (p = 0.068). The plans we expected to have higher quality (based on our geospatial analysis of forest fragmentation over time) scored significantly higher on the overall quality of forest protection than the group predicted to have lower quality. For each of the sub-criteria, the predicted high-quality plan group had higher average scores. However, these differences were not statistically different for most sub-criteria.

The most significant difference between the groups was in the Policies sub-criteria. The predicted high-quality plan group (i.e., the low-FFD group) had an average score of 9.08, whereas the predicted low-quality plan group (i.e., the high-FFD group) had an average score of 6.86. The difference was significant at the 0.05 level (p = 0.018). The next most significant difference was in the category of Internal Consistency, which examines coherence across vision, issues, goals, policies, and implementation mechanisms. The two groups’ average scores differed at the 0.1 level among the two groups, with the predicted high-quality plan group (the low-FFD group) scoring 3.08 and the predicted low-quality plan group (the high-FFD group) scoring 2.43 (p = 0.078). There were no other significant differences in the two groups’ average sub-criteria scores.

3.2. A Closer Look at Policies

We further investigated potential differences in specific policies between the groups, partly because of the most significant differences found in these sub-criteria and partly because of the breadth within them. The Policies sub-criteria involve specific policies to protect forestland in the areas of land use planning, regulations, organizational coordination, financing, and public awareness (D1–D8; Table 2). They also include a design component (e.g., future land use plan; D9) that may help the implementation of these policies and an extra credit criterion (D10) to account for any additional policy that is not included in D1–D8.

Among these sub-criteria, directing development to already disturbed areas (D3) was the most common policy included in the municipal plans (Table 4). Twelve of the 13 plans in the predicted high-quality plan group (i.e., the low-FFD group) and all 14 plans in the predicted low-quality plan group (i.e., the high-FFD group) specified that new development should occur in the already disturbed areas. In other words, all but one plan in the overall sample included a policy of directing new land development to already disturbed areas. The next most common policies in our sample of 27 plans were providing spatial designs, such as a future land use map (D9) and restricting development in environmentally sensitive areas (D2). More than three-quarters of the plans included these two policies. In addition, two-thirds of the communities had policies on conservation subdivision design or cluster development (D4), which are alternative design techniques used to guide residential development in suburban, exurban, and rural areas. On the other end of the spectrum, the least commonly included policies were the identification of financing mechanisms (D6), followed by organizational partnerships (D5) to facilitate forestland protection and acquisition. One-quarter and one-third of the plans, respectively, covered these policies.

For the most part, more low-FFD communities than high-FFD communities included a particular policy. This is consistent with our hypothesis that the low-FFD group would have higher-quality plans than the high-FFD group. There were three exceptions: high-FFD communities more frequently included policies that direct development to already disturbed areas (D3), public awareness efforts (D8), and spatial design to help protect forestland (D9). However, the low-FFD communities followed closely in addressing these policies. The highest percentage difference in coverage of these policies was 12%, with 43% of the high-FFD group versus 31% of the low-FFD group having programs to improve public awareness of forest fragmentation or protection (D8). In the rest of the policies, the proportional differences were sometimes quite stark. For instance, for D1 (requiring forest dedication or protection), D6 (identifying financial mechanisms for forest protection), and D10 (additional policies), the percentage difference between the groups exceeded 30%, such that the low-FFD group incorporated a particular policy in their plans to a much greater extent than the high-FFD group.

The analysis of whether a policy exists in a comprehensive plan is helpful, but a simple count analysis does not provide information on the strength of a particular policy (i.e., whether it is thorough, action-oriented, and mandatory, as opposed to suggested). For example, consider the difference between the following D3 (“direct development to already disturbed areas”) policies. Menasha’s plan uses suggestive language: “Encourage urban in-fill … or redevelopment in areas where urban services are already in place” ([50], p. 261). In contrast, La Pointe’s plan uses mandatory language: “Use existing land designated for urban uses before permitting development within or adjacent to forested lands” ([51], p. 22).

Our examination of the average score of a policy followed a pattern similar to the count analysis. Often, the score was higher for low-FFD communities’ plans, with the exception of D3, D8, and D9, as in the count analysis (Table 4). Furthermore, we found significant differences in three policies’ average scores between the groups: requiring forest dedication or protection (D1), identifying financial mechanisms for forest protection (D6), and additional policies (D10). For each of these policies, low-FFD communities with plans that we expected to have higher quality had significantly higher average policy scores than high-FFD communities.

4. Discussion

Our study, which adapted the plan quality analysis framework by Berke and Godschalk [44] for community-based forest protection and examined the influence of municipal comprehensive planning on forest fragmentation, showed that the quality of comprehensive plans with respect to forest protection makes a difference in mitigating forest fragmentation. In two groups of municipal plans, a sample derived from a study of forest fragmentation dynamics between 2001–2011, we found that communities that have experienced lower-than-expected change in forest fragmentation (i.e., low-FFD communities) had significantly higher plan scores than communities that have experienced greater-than-expected forest fragmentation change (i.e., high-FFD communities). We hypothesized that this would be the case.

The plan quality analysis further showed that the statistical differences between the two groups of municipal plans arose primarily from the Policies sub-criteria. The low-FFD group had an average score of 9.08, 2.22 points higher than that of the high-FFD group (with the difference significant at the 0.05 level). It is not surprising to find significant differences in the expected direction in the Policies sub-criteria. A number of studies have shown that forest conservation policies make a difference in the protection of forestland. For instance, in a review of more than 150 studies, Mid-Venditti et al. [52] showed that community-based management, which includes municipal determination of forestland to be protected, is associated with positive impacts on forestland in over 80% of cases. Göçmen [53] found that conservation subdivision design was significantly more effective than conventional subdivision design in protecting regionally important resources, including forestland. Liu et al. [23] found targeted policies around natural resources to have positive and significant impacts on mitigating forest fragmentation. In examining how effectively policies protected forestland, Börner et al. [54] emphasized the importance of geographic context, as the same policy may have different results in different areas. For example, consider municipal policies that direct growth. Despite findings that leapfrog development outside urban growth boundaries has negatively impacted forestland elsewhere [29,30], communities that have implemented boundaries to direct development into already disturbed areas were able to protect forestland in their communities [27,28].

The second most significant difference between the two groups was in the Internal Consistency category, which examines coherence across vision, issues, goals, policies, and implementation mechanisms. The average scores of the two groups differed at the 0.1 level, with the low-FFD group scoring significantly higher. It is not surprising to find strong internal consistency among plans with strong policies because the internal consistency sub-criteria can be related to policies. For example, policies can be clearly linked back to goals and forward to implementation actions to address forest fragmentation or preservation.

There were no other significant differences in the average sub-criteria scores between the two groups. The lack of significant differences in other criteria may not be surprising, considering that comprehensive plans are formulated under the State guidelines, including required elements and a relatively consistent format. In particular, state requirements and guidelines likely contribute to the similarity of the two groups’ scores in the “implementation”, “monitoring and evaluation”, “interorganizational coordination”, and “compliance” sub-criteria. All comprehensive plans in Wisconsin are required to include nine elements and be updated within 10 years. In addition, Wisconsin’s Comprehensive Planning Law is intended to promote compact development, preserve natural lands, and encourage the redevelopment of existing developed lands. Under this guidance, almost all local comprehensive plans state that the preservation of natural resources (e.g., forestlands) is one of the community’s primary goals, which results in the two groups’ highly consistent scores in “issues and vision” and “goals”.

We should note that even though the two groups showed significant differences in Policies, neither group had a particularly high average policy score. The low-FFD group, which had significantly higher Policies scores, achieved only about 41% of the potential Policy score in our assessment benchmark. This could be because forest protection may be just one of many municipal goals, and policies to support the municipality’s other goals may differ considerably. For instance, increasing the tax base (and hence allowing development) can be a primary goal that takes priority over forest protection. Additionally, when development pressure is high, municipal governments may have to allow or encourage land development that will likely deteriorate natural resources. Even when municipalities do value forest protection, they may not be able to include many policies in their comprehensive plans because of the costs associated with implementing those policies.

Future work in this area could examine the effectiveness of the different types of policies found in comprehensive plans. Our study investigated the strength of comprehensive plans but did not investigate the effectiveness of each type of policy in mitigating forest fragmentation.

Future studies could also investigate the relationship between forest change dynamics and the quality of other types of municipal plans. Many municipalities, especially those that have planning capacity, adopt a number of plans, including master land use, sustainability, resilience, green infrastructure, and open space plans. Some of these plans, such as green infrastructure or open space plans, may be where a community states most of its goals, visions, and policies for its forestland. In that case, the community may address forest fragmentation more fully in these plans than in its comprehensive plan. However, investigating other municipal plans presents additional challenges. First, not every community is equipped to prepare different plans [55], so the sample of available plans might be rather small. Many small communities do not have “planning” staff; sometimes, the part-time village/town administrator administers all of the community’s planning and zoning responsibilities (as well as other responsibilities). (Our sample includes municipalities of different sizes and services: incorporated areas of villages and cities and unincorporated areas of towns. In Wisconsin, cities have the largest population sizes on average, followed by villages and towns. In an additional analysis, we examined plan quality score differences across jurisdiction types and found significant differences in the mean total scores between cities and villages; there were no other significant differences in the mean total scores between jurisdiction types. When we looked into different criteria, we found significant differences in A (issues and vision statement), C (goals), D (policies), and F (monitoring and evaluations) among cities and villages. This may indicate the organizational resources available to cities, as opposed to their smaller counterparts of villages.) Wisconsin’s Comprehensive Planning mandate, associated funds, and technical assistance have provided the impetus for many jurisdictions to adopt a comprehensive plan. Without a similar mandate to adopt other types of plans, municipalities may not have the incentive or rationale to adopt them. Second, obtaining different plans could be much more difficult in the absence of a library of plans like the digital collection of comprehensive plans provided by the Wisconsin Department of Administration. Future studies could also investigate updated comprehensive plans; however, in this kind of work, investigating original plans along with updates would be critical to seeing potential changes in community priorities (see [56]).

5. Conclusions

In closing, we would like to highlight a significant contribution of this study. To the best of our knowledge, the relationship between the quality of municipal-based plans and forest fragmentation dynamics has not been studied previously; indeed, there is a dearth of evidence on the effectiveness of (the quality of) comprehensive plans in municipal matters in general [57]. In the earlier phase of this study, which examined factors that contributed to municipal forest fragmentation dynamics in Wisconsin, we did not find the mere adoption of a municipal comprehensive plan to be an important factor in mitigating forest fragmentation. The finding that the quality of comprehensive plans makes a difference in the forest fragmentation dynamics—and therefore in the likely mitigation of forest fragmentation—is a significant contribution to the literature and to efforts to create environmentally sustainable communities.

Author Contributions

Conceptualization, Y.Z. and Z.A.G.; Methodology, Y.Z.; Formal Analysis, Y.Z. and Z.A.G.; Investigation, Y.Z.; Data Curation, Y.Z.; Writing—Original Draft Preparation, Y.Z.; Writing—Review & Editing, Z.A.G. and Y.Z.; Visualization, Y.Z.; Supervision, Z.A.G.; Project Administration, Y.Z.; Funding Acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. All the comprehensive plans reviewed in this study were acquired from individual municipalities’ websites; an inventory of most recent plans are hosted by the Wisconsin Department of Administration at: https://doa.wi.gov/Pages/LocalGovtsGrants/Comprehensive-Planning-Library-of-Plans.aspx.

Acknowledgments

We thank Ruanda McFerron for research assistance, Jim LaGro, Volker Radeloff, Kurt Paulsen, and Steve Ventura for useful suggestions in different stages of the study, and Julie Steiff and three anonymous reviewers for their insightful feedback on an earlier version of this manuscript.

Conflicts of Interest

Author Yan Zhang was employed by the company Palmetto Posting Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

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Tables

Table 1

Selected Municipal Comprehensive Plans for Quality Evaluation.

Municipal Comprehensive Plans that Are Expected to Have High Quality (i.e., Low Forest Fragmentation Dynamics; Group I)

Municipal Comprehensive Plans that Are Expected to Have Low Quality (i.e., High Forest Fragmentation Dynamics; Group II)

City of Amery, Polk CountyCity of Fond du Lac, Fond du Lac CountyCity of Hartford, Washington/Dodge CountyCity of Menasha, Winnebago/Calumet CountyCity of New London, Waupaca/Outagamie CountyCity of Oshkosh, Winnebago CountyCity of Pittsville, Wood CountyCity of Verona, Dane CountyTown of Bayfield, Bayfield CountyTown of La Pointe, Ashland CountyTown of Neenah, Winnebago CountyTown of Nokomis, Oneida County Village of Grafton, Ozaukee CountyVillage of Shiocton, Outagamie County

City of Darlington, Lafayette CountyCity of Green Bay, Brown CountyCity of La Crosse, La Crosse CountyCity of New Berlin, Waukesha CountyCity of Stevens Point, Portage CountyCity of Washburn, Bayfield CountyTown of Bass Lake, Sawyer CountyTown of Brookfield, Waukesha CountyTown of Sharon, Walworth CountyVillage of Centuria, Polk CountyVillage of Coleman, Marinette CountyVillage of Curtiss, Clark CountyVillage of Paddock Lake, Kenosha CountyVillage of Pulaski, Brown/Oconto/Shawano CountyVillage of West Baraboo, Sauk County

Table 2

Evaluation Criteria for Comprehensive Plan Quality for Assessing Mitigation of Forest Fragmentation in Wisconsin Communities.

Internal characteristics

A. Issues and vision statement

A1. Is there a review of forest fragmentation (or related issues, such as urban sprawl) as a primary problem and issue in the community?

A2. Is there a preliminary assessment of the major trends and impacts of forecasted forest fragmentation (or related issues) for a future planning period?

A3. Is there a description of major opportunities for or threats to mitigating forest fragmentation (or related issues)?

A4. Is there a description of a community vision for forestland protection?

B. Fact base

B1. Is there a statement on the existing forestland composition and configuration?

B2. Is there a map or statement on the delineation of the location of forest fragmentation (or related issues)?

B3. Is there a map or statement on the delineation of the magnitude of forest fragmentation (or related issues)?

B4. Are maps that display forest fragmentation (or related issues) clear, relevant, and comprehensible?

B5. Are tables that aggregate data on forest fragmentation (or related issues) relevant and meaningful?

B6. Are baseline spatial data and inventories adequate?

C. Goals

C1. Is mitigation of forest fragmentation (or related issues) stated as a primary goal of the community?

D. Policies: Does the plan…

D1. require forest dedication or protection?

D2. restrict development in environmentally sensitive areas?

D3. direct development to already disturbed areas?

D4. include policies on conservation subdivision design/cluster development?

D5. address partnerships with nongovernmental organizations to acquire and protect forestlands?

D6. identify sources for financing to facilitate forestland protection and acquisition?

D7. have policies on PDR, TDR and other market mechanisms to protect forestland?

D8. have an information-gathering and education program to improve public awareness of forest fragmentation/protection?

D9. have spatial designs that specify future land use, infrastructure, transportation, and open space networks that help protect forestland?

D10. have additional policies that may help protect forestland?

E. Implementation

E1. Are there timelines for actions to carry out these forest fragmentation or protection-related policies?

E2. Are organizations that are responsible for actions identified?

E3. Are sources of funding identified to support actions?

E4. Is there a timetable or time frame for updating the plan?

F. Monitoring and evaluation

F1. Are forest fragmentation or protection-related goals based on measurable objectives?

F2. Are indicators of each objective included?

F3. Are organizations that are responsible for monitoring and/or providing data for indicators identified?

F4. Is there a timetable for updating the plan based partially on the results of monitoring changing conditions?

G. Internal consistency

G1. Do goals accommodate forest fragmentation or protection-related issues and vision?

G2. Are policies clearly linked back to goals and forward to implementation actions in terms of forest fragmentation or protection?

G3. Does monitoring include indicators to gauge the goal achievement and effectiveness of forest-protection policies?

External characteristics

H. Organization and presentation

H1. Is there a glossary of terms or definitions of forest fragmentation or protection?

H2. Are there clear illustrations for forest fragmentation or protection (e.g., maps, charts, pictures, and diagrams)?

H3. Is spatial information clearly illustrated on maps of forest fragmentation or protection?

H4. Are supporting documents (e.g., videos, CD, GIS, website) included with the plan on forest fragmentation or protection?

I. Interorganizational coordination

I1. Is there vertical coordination with the forest-related plans or policies of federal, state, and regional parties?

I2. Is there horizontal coordination with the forest-related plans or policies of other local parties within or outside the local jurisdiction?

J. Compliance

J1. Does the plan include the nine elements required by the Wisconsin Comprehensive Planning Law (Issues and Opportunities; Housing; Transportation; Utilities and Community Facilities; Agricultural, Natural, and Cultural Resources; Economic Development; Intergovernmental Cooperation; Land Use; Implementation)?

J2. Do the required elements fit together?

Table 3

Average Scores of Comprehensive Plans based on Forest Fragmentation Dynamics.

Evaluation Criteria	Group I(Low Forest Fragmentation Dynamics)	Group II(High Forest Fragmentation Dynamics)	Highest Possible Score
A. Issues and vision	2.15	1.57	8
B. Fact base	5.00	4.07	12
C. Goals	1.54	1.36	2
D. Policies **	9.08	6.86	22
E. Implementation	4.00	3.36	8
F. Monitoring and evaluation	2.46	2.21	8
G. Internal consistency *	3.08	2.43	6
H. Organization and presentation	2.08	1.43	8
I. Interorganizational coordination	2.00	1.93	4
J. Compliance	3.85	3.71	4
Total *	50.85	41.23	108

Notes: (1) One criterion under D (D10) provides extra credit for any additional policies that may help mitigate forest fragmentation that we did not include in the original evaluation protocol. In evaluating D10, we followed a similar protocol to the original scoring scheme: we evaluated each additional policy for up to 2 points. Therefore, theoretically speaking, there was no highest score for criterion D. The highest possible score of 22 for Policies (D) in this table is based on our sample. (2) The highest possible total plan score reflects our weighting scheme of providing double points for fact base, goals, and policies. (3) * indicates a significant difference between groups at the 0.1 level; ** indicates a significant difference at the 0.05 level.

Table 4

The Presence of and Average Score for Each Policy in the Low/high Forest Fragmentation Dynamic Groups.

	Number of Plans That Adopted the Policy			Average Score across Plans
Policy	Low Fragmentation Dynamics(n = 13)	High Fragmentation Dynamics(n = 14)	Total(n = 27)	Low Fragmentation Dynamics	High Fragmentation Dynamics
D1 (forest dedication) *	8	4	12	0.85	0.36
D2 (away from envtl sensitive)	11	10	21	1.46	1.00
D3 (direct devt to disturbed areas)	12	14	26	1.15	1.43
D4 (conservation subdivision)	9	9	18	1.00	0.71
D5 (organization partnership)	6	3	9	0.46	0.43
D6 (financing) **	5	1	6	0.62	0.07
D7 (market mechanisms)	6	4	10	0.54	0.36
D8 (public awareness)	4	6	10	0.54	0.64
D9 (spatial design)	10	12	22	0.77	1.07
D10 (additional policies) *	8	3	13	1.77	0.79

Note: * indicates significant difference between the average scores of the two groups at the 0.1 level; ** indicates significant difference between the average scores of the two groups at the 0.05 level.

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