Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies

Abstract

h i g h l i g h t s• We explored urbanization scenarios based on hypothetical land use policies.• We used a unique modeling method to represent conservation planning strategies.• No single strategy was best for achieving all conservation goals.• Effective planning requires assessment of tradeoffs between differing priorities. a b s t r a c tLand that is of great value for conservation can also be highly suitable for human use, resulting in competition between urban development and the protection of natural resources. To assess the effectiveness of proposed regional land conservation strategies in the context of rapid urbanization, we measured the impacts of simulated development patterns on two distinct conservation goals: protecting priority natural resources and limiting landscape fragmentation. Using a stochastic, patch-based land change model (FUTURES) we projected urbanization in the North Carolina Piedmont according to status quo trends and several conservation-planning strategies, including constraints on the spatial distribution of development, encouraging infill, and increasing development density. This approach allows simulation of population-driven land consumption without excluding the possibility of development, even in areas of high conservation value. We found that if current trends continue, new development will consume 11% of priority resource lands, 21% of forested land, and 14% of farmlands regionally by 2032. We also found that no single conservation strategy was optimal for achieving both conservation goals. For example, strategies that excluded development from priority areas caused increased fragmentation of forests and farmlands, while infill strategies increased loss of priority resources proximal to urban areas. Exploration of these land change scenarios not only confirmed that a failure to act is likely to result in irreconcilable losses to a conservation network, but that all conservation plans are not equivalent in effect, highlighting the importance of analyzing tradeoffs between alternative conservation planning approaches.

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

“…FUTURES, GEOMOD, and LCM allow for alternative scenarios depicting different quantity of change estimates. For example, scenarios depicting higher or lower projections of per capita land consumption can simulate dense new development or increased land consumption [39,58]. FUTURES uniquely offers the ability to simulate management policies related to the importance of placing new development near existing urban area (e.g., sprawl vs. infill).…”

“…The DEMAND sub-model is a user defined quantity of land converted to developed, based on the relationship between population change and the amount of land developed during the calibration period [39]. To determine the location for POTENTIAL urban development to occur, FUTURES requires a probability surface generated using any site suitability modeling technique [39,58]. PGA employs an iterative, stochastic site selection process and a patch-based region growing algorithm designed to mimic distinct spatial patterns from a library of observed patch patterns.…”

“…We used a multilevel mixed-effects model to predict the conversion of undeveloped land to urban development based on environmental, infrastructure and socio-economic variables, assuming stationarity over time in the variables influencing change [39,58]. We used sampled points (n = 1450) to estimate model parameters for the probability that an undeveloped cell, i, becomes developed as:…”

“…Development pressure is created using a distance decay function from existing urban locations. The intercept and development pressure varied by county to account for differences between jurisdictional boundaries [39,58]. Results of the multi-level model were used for site suitability across the three models.…”

Land management institution as a key confinement of urbanization in Baotou, China⿿Application of proposed endogenous urbanization model

“…For example, Xiao et al (2006) categorized spatial patterns into special objectives, social-political intervention, and normal urban growth. (2) Studies have quantified the driving forces of urban expansion by using principle component analysis, multiple linear regression, logistic regression, spatial regression, panel models, and stochastic cellular automata models (Kuang et al, 2009;Seto et al, 2011;Upton et al, 2014;Dorning et al, 2015). Here, population growth, urbanization, industrialization, economic growth, technological progress, physical conditions, marketization, and land use policies have been tested as the major drivers (He et al, 2008;Li et al, 2011).…”