Matthew Marsik

Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning

People around the world are looking to marine ecosystems to provide additional benefits to society. As they consider expanding current uses and investing in new ones, new management approaches are needed that will sustain the delivery of the diverse benefits that people want and need. An ecosystem services framework provides metrics for assessing the quantity, quality, and value of benefits obtained from different portfolios of uses. Such a framework has been developed for assessments on land, and is now being developed for application to marine ecosystems. Here, we present marine Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), a new tool to assess (i.e., map, model, and value) multiple services provided by marine ecosystems. It allows one to estimate changes in a suite of services under different management scenarios and to investigate trade-offs among the scenarios, including implications of drivers like climate. We describe key inputs and outputs of each of the component ecosystem service models and present results from an application to the West Coast of Vancouver Island, British Columbia, Canada. The results demonstrate how marine InVEST can be used to help shape the dialogue and inform decision making in a marine spatial planning context.

Roads as drivers of change: Trajectories across the Tri-National Frontier in MAP, the Southwestern Amazon

Regional studies of land cover change are often limited by available data and in terms of comparability across regions, by the transferability of methods. This research addresses the role of roads and infrastructure improvements across a tri-national frontier region with similar climatic and biophysical conditions but very different trajectories of forest clearing. The standardization of methodologies and the extensive spatial and temporal framework of the analysis are exciting as they allow us to monitor a dynamic region with global significance as it enters an era of increased road connectivity and massive potential forest loss. Our study region is the “MAP” frontier, which covers Madre de Dios in Peru, Acre in Brazil, and Pando in Bolivia. This tri-national frontier is being integrated into the global economy via the paving of the Inter-Oceanic Highway which links the region to ports in the Atlantic and Pacific, constituting a major infrastructure change within just the last decade. Notably, there are differences in the extent of road paving among the three sides of the tri-national frontier, with paving complete in Acre, underway in Madre de Dios, and incipient in Pando. Through a multi-temporal analysis of land cover in the MAP region from 1986 to 2005, we found that rates of deforestation differ across the MAP frontier, with higher rates in Acre, followed by Madre de Dios and the lowest rates in Pando, although the dominant land cover across the region is still stable forest cover (89% overall). For all dates in the study period, deforestation rates drop with distance from major roads although the distance before this drop off appears to relate to development, with Acre influencing forests up to around 45 km out, Madre de Dios to about 18 km out and less of a discernable effect or distance value in Pando. As development occurs, the converted forest areas saturate close to roads, resulting in increasing rates of deforestation at further distances and patch consolidation of clearings over time. We can use this trend as a basis for future change predictions, with Acre providing a guide to likely future development for Madre de Dios, and in time potentially for Pando. Given the correspondence of road paving to deforestation, our findings imply that as road paving increases connectivity, flows of people and goods will accelerate across this landscape, increasing the likelihood of dramatic future changes on all sides of the tri‑national frontier.

Amazon deforestation: Rates and patterns of land cover change and fragmentation in Pando, northern Bolivia, 1986 to 2005:

Much research has focused on deforestation in the Amazon, particularly with proximity to roads and population centers as proximate causes. This research presents the analysis of rates and patterns of land cover change in Pando, northern Bolivia, an area with most of its tropical humid forest still intact. Using a decision tree classifier, five forest/non-forest (FNF) classifications were created for 1986, 1991, 1996, 2000, and 2005 from 40 Landsat images that were preprocessed and mosaicked. FNF trajectory images were created for each date pair to indicate areas of stable forest and non-forest, and areas and rates of de/reforestation. Mean patch size, perimeter-area ratio, fractal dimension, and aggregation index metrics were calculated for the FNF trajectory images based on increasing buffer distances from road and along the main access road. In 2005, forest covered 95% of the area in Pando. Large areas of aggregated deforestation occur nearest the department capital of Cobija, along the border with Brazil, and about 50 km west and east of Cobija along the principal access road. Deforestation becomes patchier with increased distance from the population center and laterally from the road. Multiple non-linear relationships exist between the fragmentation metrics and distance from road. The results have implications for understanding and managing the spatial contiguity of these forests, which provide valuable ecological services as well as the livelihood base for many inhabitants.

Scaling categorical spatial data for earth systems models

Efforts to deduce the appropriate scales of ecosystem functions and how patterns change with scale have a long history in ecology and landscape ecology (Levin, 1992; O’Neill et al., 1996). Ecosystem function models are critical to predicting ecosystem responses to global change, but are limited by the technical challenges of model–data synthesis. Accurately relating phenomena across multiple scales is an important challenge in ecological modeling, as information is lost when converting between scales of analysis. Researchers must determine how much information is necessary to preserve the landscape signature of the ecological processes under study. Zhao & Liu (2014) sought to determine the appropriate spatial resolution for categorical land cover data to use in regional-scale models of carbon dynamics, and compared the use of two common categorical data resampling methods: majority (MR) and nearest neighbor (NNR). Their analysis of the NNR method showed a power-law relationship between study extent and grain, but results from MR method showed a different relationship, suggesting that the resampling method drove the results. Zhao & Liu (2014) concluded the NNR method to be superior and reported the MR approach produced ‘devastatingly deficient’ results. We discuss the lack of robustness of their power-law relationship by analyzing the configuration and composition of simulated landscapes subjected to different resampling methods. The authors stated that NNR is clearly preferential to the MR method because NNR preserves uncommon land cover types. They support their use of NNR by mis-citing Cain et al. (1997). Zhao & Liu (2014) state that the critical spatial resolution in scaling exercises follows a power-law function of the study region extent. We argue that the pattern of the landscape process to be modeled determines the results of the resampling method. We illustrate, using a simple simulated landscape, how the effect of resampling algorithm is related to the proportion of landscape within each land cover class and the spatial configuration (clumpiness)

Linking Spatial and Temporal Variation at Multiple Scales in a Heterogeneous Landscape

Abstract Anthropogenic, ecological, and land-surface processes interact in landscapes at multiple spatial and temporal scales to create characteristic patterns. The relationships between temporally and spatially varying processes and patterns are poorly understood because of the lack of spatiotemporal observations of real landscapes over significant stretches of time. We report a new method for observing joint spatiotemporal landscape variation over large areas by analyzing multitemporal Landsat data. We calculate the spatiotemporal variation of the Normalized Difference Vegetation Index (NDVI) in the area covered by one Landsat scene footprint in north central Florida, over spatial windows of 104–108 m2 and time steps of two to sixteen years. The correlations, slopes, and intercepts of spatial versus temporal regressions in the real landscape all differ significantly from results obtained using a null model of a randomized landscape. Spatial variances calculated within windows of 105–107 m2 had the strongest relationships with temporal variances (regressions with both larger and smaller windows had lower coefficients of determination), and the relationships were stronger with longer time steps. Slopes and y-intercepts increased with window size and decreased with increased time step. The spatial and temporal scales at which NDVI signals are most strongly related may be the characteristic scales of the processes that most strongly determine landscape patterns. For example, the important time and space windows correspond with areas and timing of fires and tree plantation harvests. Observations of landscape dynamics will be most effective if conducted at the characteristic scales of the processes, and our approach may provide a tool for determining those scales.

Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest

Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software.