Wetland features and landscape context predict the risk of wetland habitat loss

Abstract

Wetlands generally provide significant ecosystem services and function as important harbors of biodiversity. To ensure that these habitats are conserved, an efficient means of identifying wetlands at risk of conversion is needed, especially in the southern United States where the rate of wetland loss has been highest in recent decades. We used multivariate adaptive regression splines to develop a model to predict the risk of wetland habitat loss as a function of wetland features and landscape context. Fates of wetland habitats from 1992 to 1997 were obtained from the National Resources Inventory for the U.S. Forest Service's Southern Region, and land-cover data were obtained from the National Land Cover Data. We randomly selected 70% of our 40 617 observations to build the model (n = 28 432), and randomly divided the remaining 30% of the data into five Test data sets (n = 2437 each). The wetland and landscape variables that were important in the model, and their relative contributions to the model's predictive ability (100 = largest, 0 = smallest), were land-cover/ land-use of the surrounding landscape (100.0), size and proximity of development patches within 570 m (39.5), land ownership (39.1), road density within 570 m (37.5), percent woody and herbaceous wetland cover within 570 m (27.8), size and proximity of development patches within 5130 m (25.7), percent grasslands/herbaceous plants and pasture/hay cover within 5130 m (21.7), wetland type (21.2), and percent woody and herbaceous wetland cover within 1710 m (16.6). For the five Test data sets, Kappa statistics (0.40, 0.50, 0.52, 0.55, 0.56; P < 0.0001), area-under-the-receiver-operating-curve (AUC) statistics (0.78, 0.82, 0.83, 0.83, 0.84; P < 0.0001), and percent correct prediction of wetland habitat loss (69.1, 80.4, 81.7, 82.3, 83.1) indicated the model generally had substantial predictive ability across the South. Policy analysts and land-use planners can use the model and associated maps to prioritize at-risk wetlands for protection, evaluate wetland habitat connectivity, predict future conversion of wetland habitat based on projected land-use trends, and assess the effectiveness of wetland conservation programs.

Widespread sampling biases in herbaria revealed from large‐scale digitization

“…Forman & Alexander, ; Hui et al ., ; Griffith et al ., ; Li et al ., ) and botanists and herbaria predominate in cities, specimens collected in proximity to either are unlikely to represent a random sample across species distributions. Specifically, species collected along roadsides are likely to be overrepresented by species that thrive with disturbance, and underrepresented by forest interior and wetland species that are harmed by disturbance (Gutzwiller & Flather, ; Rivers‐Moore & Cowden, ). As the road network continues to expand and as people become ever more concentrated in cities, this bias toward collecting near roads might become stronger in coming decades.…”

Genetic diversity in three endangered pitcher plant species (Sarracenia; Sarraceniaceae) is lower than widespread congeners

“…Wetlands are particularly at risk, containing one‐third of the threatened and endangered species in the United States (Murdock, 1994). While the southeastern United States has more area of wetlands than any other region of the country, it has also suffered the greatest loss of wetlands of any region (Gutzwiller and Flather, 2011; Herrington, 2004). Reasons include agriculture, which has traditionally been the leading cause, as well as clearing for development, which has become an ever‐increasing source of wetland loss (Brady and Flather, 1994; Gutzwiller and Flather, 2011).…”

Evaluating Alternative Temporal Survey Designs for Monitoring Wetland Area and Detecting Changes Over Time in California

“…Therefore, wetland loss rates in the Bay Area were slightly lower and less dependent on the impact scenario, suggesting that wetland loss prevention could be achieved through careful sighting of new protected areas. While this is an obviously intuitive result, we note that this approach may not be the most effective overall given the logistical and practical difficulties associated with designating new protected areas (Gutzwiller and Flather, ). These results should be considered preliminary analysis, useful for developing hypotheses for more detailed analysis.…”