Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation

The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999–2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified ‘stepping stone’ water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as ‘rich get richer’ algorithm. The ‘stepping stone’ water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as ‘stepping stone’ over time may help prioritize surface water bodies that are essential for maintaining regional scale connectivity

SCP_shapefiles

SCP_shapefile

Data from: Surface-water dynamics and land use influence landscape connectivity across a major dryland region

Landscape connectivity is important for the long-term persistence of species inhabiting dryland freshwater ecosystems, with spatiotemporal surface-water dynamics (e.g., flooding) maintaining connectivity by both creating temporary habitats and providing transient opportunities for dispersal. Improving our understanding of how landscape connectivity varies with respect to surface-water dynamics and land use is an important step to maintaining biodiversity in dynamic dryland environments. Using a newly available validated Landsat TM and ETM+ surface-water time series, we modelled landscape connectivity between dynamic surface-water habitats within Australia's 1 million km2 semi-arid Murray Darling Basin across a 25-year period (1987 to 2011). We identified key habitats that serve as well-connected ‘hubs’, or ‘stepping-stones’ that allow long-distance movements through surface-water habitat networks. We compared distributions of these habitats for short- and long-distance dispersal species during dry, average and wet seasons, and across land-use types. The distribution of stepping-stones and hubs varied both spatially and temporally, with temporal changes driven by drought and flooding dynamics. Conservation areas and natural environments contained higher than expected proportions of both stepping-stones and hubs throughout the time series; however, highly modified agricultural landscapes increased in importance during wet seasons. Irrigated landscapes contained particularly high proportions of well-connected hubs for long-distance dispersers, but remained relatively disconnected for less vagile organisms. The habitats identified by our study may serve as ideal high-priority targets for land-use specific management aimed at maintaining or improving dispersal between surface-water habitats, potentially providing benefits to biodiversity beyond the immediate site scale. Our results also highlight the importance of accounting for the influence of spatial and temporal surface-water dynamics when studying landscape connectivity within highly variable dryland environments

Response of switchgrass yield to future climate change

A climate envelope approach was used to model the response of switchgrass, a model bioenergy species in the United States, to future climate change. The model was built using general additive models (GAMs), and switchgrass yields collected at 45 field trial locations as the response variable. The model incorporated variables previously shown to be the main determinants of switchgrass yield, and utilized current and predicted 1 km climate data from WorldClim. The models were run with current WorldClim data and compared with results of predicted yield obtained using two climate change scenarios across three global change models for three time steps. Results did not predict an increase in maximum switchgrass yield but showed an overall shift in areas of high switchgrass productivity for both cytotypes. For upland cytotypes, the shift in high yields was concentrated in northern and north-eastern areas where there were increases in average growing season temperature, whereas for lowland cultivars the areas where yields were projected to increase were associated with increases in average early growing season precipitation. These results highlight the fact that the influences of climate change on switchgrass yield are spatially heterogeneous and vary depending on cytotype. Knowledge of spatial distribution of suitable areas for switchgrass production under climate change should be incorporated into planning of current and future biofuel production. Understanding how switchgrass yields will be affected by future changes in climate is important for achieving a sustainable biofuels economy

Spatial and Temporal Heterogeneity of Agricultural Fires in the Central United States in Relation to Land Cover and Land Use

Agricultural burning is an important land use practice in the central U.S. but has received little attention in the literature, whereas most of the focus has been on wildfires in forested areas. Given the effects that agricultural burning can have on biodiversity and emissions of greenhouse gasses, there is a need to quantify the spatial and temporal patterns of fire in agricultural landscapes of the central U.S. Three years (2006–2008) of the MODIS 1 km daily active fire product generated from the MODIS Terra and Aqua satellite data were used. The 2007 Cropland Data Layer developed by the U.S. Department of Agriculture was used to examine fire distribution by land cover/land use (LCLU) type. Global ordinary least square (OLS) models and local geographically weighted regression (GWR) analyses were used to explore spatial variability in relationships between fire detection density and LCLU classes. The monthly total number of fire detections peaked in April and the density of fire detections (number of fires/km2/ 3 years) was generally higher in areas dominated by agriculture than areas dominated by forest. Fire seasonality varied among areas dominated by different types of agriculture and land use. The effects of LCLU classes on fire detection density varied spatially, with grassland being the primary correlate of fire detection density in eastern Kansas; whereas wheat cropping was important in central Kansas, northeast North Dakota, and northwest Minnesota

Local graph theory connectivity metrics from Bishop-Taylor et al. 2017

Graph theory local connectivity metrics data for two dispersal distances and each seasonal time-step in the 1987-2011 Landsat-derived surface-water time series (Tulbure et al. 2016). Betweenness and degree centrality results were used to assess the distribution of important “stepping-stone” and “hub” habitats across Australia’s Murray-Darling Basin

Inventory and Ventilation Efficiency of Nonnative and Native Phragmites australis (Common Reed) in Tidal Wetlands of the Chesapeake Bay

Nonnative Phragmites is among the most invasive plants in the U.S. Atlantic coast tidal wetlands, whereas the native Phragmites has declined. Native and nonnative patches growing side by side provided an ideal setting for studying mechanisms that enable nonnative Phragmites to be a successful invader. We conducted an inventory followed by genetic analysis and compared differences in growth patterns and ventilation efficiency between adjacent native and nonnative Phragmites stands. Genetic analysis of 212 patches revealed that only 14 were native suggesting that very few native Phragmites populations existed in the study area. Shoot density decreased towards the periphery of native patches, but not in nonnative patches. Ventilation efficiency was 300 % higher per unit area for nonnative than native Phragmites, likely resulting in increased oxidation of the rhizosphere and invasive behavior of nonnative Phragmites. Management of nonnative Phragmites stands should include mechanisms that inhibit pressurized ventilation of shoots

Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets

Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs

Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets

Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs