Land-use, climate, and policy changes have impacted the fire regimes of many landscapes across northern Europe. Heathlands in oceanic climates are globally important ecosystems that have experienced an increase in the prevalence of destructive wildfire. Many of these landscapes are also managed using traditional prescribed burning that enhances their structural diversity and agricultural productivity. The changing role of wild and managed fire highlights a necessity to better understand the performance of fire behaviour prediction models for these ecosystems to support sustainable fire risk management. Our research evaluates the outputs of several empirical and quasi-empirical prediction models, as well as their varying software implementations, against observations of fire behaviour. The Rothermel model and its implementations predict rates of spread with similar accuracy to baseline empirical models and provide tolerable estimates of observed fire rate of spread. The generic shrubland empirical model developed by Anderson et al. consistently overpredicts observed rates of spread for prescribed burns in target fuel structures, but its predictions otherwise have a strong correlation with observed spread rate. A range of empirical models and software tools thus appear appropriate to assist managers who wish to evaluate potential fire behaviour and assess risk in heathland landscapes.

Major disturbances fundamentally alter ecosystems through the transformation of both biotic and abiotic factors. The eruption of Mount St. Helens in 1980 resulted in a cataclysmic restructuring of its surrounding landscapes. The Pumice Plain is one of these landscapes, where tree species such as Sitka willow (Salix sitchensis) and their dependent communities have established along newly-formed streams. Thus, the study of these dependent communities provides a unique opportunity to investigate factors influencing metacommunity assembly during true primary succession. We analyzed the influence of landscape connectivity on metacommunity assembly through a novel application of circuit theory, alongside the effects of other factors such as stream locations, willow leaf chemistry, and leaf area. We found that landscape connectivity structures community composition on willows across the Pumice Plain, while our other factors had varied effects. Most importantly, multiple levels of spatial habitat structure linked via landscape connectivity can predict the presence of organisms lacking high rates of dispersal, such as the invasive stem-boring poplar weevil (Cryptorhynchus lapathi). This is critical for management as we show that the maintenance of a heterogeneous mixture of landscape connectivities and resource locations can facilitate meta-community dynamics to promote ecosystem function and mitigate the influences of invasive species.

Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (>92% accuracy for S. patens regardless of imagery age). Our methods allowed us to characterize full landscapes (two geospatially proximal areas, >7,000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1-m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1,087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem-type.

Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (>92% accuracy for S. patens regardless of imagery age). Our methods allowed us to characterize full landscapes (two geospatially proximal areas, >7,000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1-m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1,087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem-type.

The eruption of Mount St. Helens in 1980 resulted in a cataclysmic restructuring of its surrounding landscapes. The Pumice Plain is one of these landscapes, where tree species such as Sitka willow (Salix sitchensis) and their dependent communities have been established along newly-formed streams. Thus, the study of these dependent communities provides a unique and rare opportunity to investigate factors influencing metacommunity assembly during true primary succession. We analyzed the influence of landscape connectivity on metacommunity assembly through a novel application of circuit theory, alongside the effects of other factors such as stream locations, willow leaf chemistry, and leaf area. We found that landscape connectivity structures community composition on willows across the Pumice Plain, where the least connected willows favored active flyers such as the western tent caterpillar (Malacosoma fragilis) or the Pacific willow leaf beetle (Pyrrhalta decora carbo). We also found that multiple levels of spatial habitat structure linked via landscape connectivity can predict the presence of organisms lacking high rates of dispersal, such as the invasive stem-boring poplar weevil (Cryptorhynchus lapathi). This is critical for management as we show that the maintenance of a heterogeneous mixture of landscape connectivity and resource locations can facilitate metacommunity dynamics to promote ecosystem function and mitigate the influences of invasive species.