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Surface water is the most readily accessible water resource and provides an array of ecosystem services, but is stressed by changes in climate, land cover, and population size. Understanding driver...

Context: Key to understanding forest water balances is the role of tree species regulating evapotranspiration (ET), but the synergistic impact of forest species composition, topography, and water availability on ET and how this shapes drought sensitivity across the landscape remains unclear.Objectives: Our aims were to quantify (1) the effect of forest composition and topography including elevation and hillslope gradients on the relationship between ET and water availability, and (2) whether the relationship has changed over time. Methods: We used remotely sensed Landsat and MODIS ET to quantify forest ET across the Blue Ridge ecoregion of the southeastern USA. Then quantified metrics describing ET responses to water availability and trends in responses over time and assessed how these metrics varied across elevation, hillslope, and forest composition gradients. Results: We demonstrated forest ET is becoming less constrained by water availability at the expense of lateral flow. Drought impacts on ET diverged along elevation and hillslope gradients, and that divergence was more pronounced with increasingly severe drought, indicating high elevation and drier, upslope regions tend to maintain ET rates even during extreme drought. We identified a decoupling of ET from water availability over time, and found this process was accelerated at higher elevations and in areas with more diffuse-porous trees. Conclusions: Given the large proportion of forests on the landscape distributed across high elevation and upslope positions, reductions in downslope water availability could be widespread, amplifying vulnerability of runoff, the health of downslope vegetation, and aquatic biodiversity.

During the 21st century, human-environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human-environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data. SESYNC under National Science Foundation [DBI-1639145]; National Science FoundationNational Science Foundation (NSF); National Science Foundation through the NEON Program Published version This article emerged from a workshop titled People, Land, & Ecosystems: Leveraging NEON for SocioEnvironmental Synthesis that was held at the National Socio-Environmental Synthesis Center (SESYNC). This work was supported by SESYNC under funding received from the National Science Foundation DBI1639145. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program. The conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.

Spatiotemporal distribution and systematic quantification of surface water and their drivers of change are critical. However, quantifying this distribution is challenging due to a lack of spatially explicit and temporally dynamic empirical data of both surface water and its drivers of change at large spatial scales. We focused on one of the largest dryland basins in the world, Australia's Murray-Darling Basin (MDB), recently identified as a global hotspot of water decline. We used a new remotely sensed time-series of surface water extent dynamics (SWD) data to quantify spatiotemporal patterns in surface water across the entire MDB and catchments and to assess natural and anthropogenic drivers of SWD, including climate and historical land use change. We show high intra- and inter-annual dynamics in surface water with a rapid loss during the Millennium Drought, the worst, decade-long drought in SE Australia. We show strong regional and catchment differences in SWD, with the northern basin showing high variability compared to the southern basin which shows a steady decline in surface water. Linear mixed effect models including climate and land-use change variables explained up to 70% variability in SWD with climate being more important in catchments of the northwestern MDB, whereas land-use was important primarily in the central MDB. Increase in fraction of dryland agriculture in a catchment and maximum temperature was negatively related to SWD, whereas precipitation and soil moisture were positively related to SWD. The fact that land-use change was an important explanatory variable of SWD in addition to climate is a significant result as land-use can be managed more effectively whereas climate-mitigation actions can be intractable, with global change scenarios predicting drier conditions for the area followed by a further reduction in surface water availability.

Detailed information on the number and density of trees is important for conservation and sustainable use of forest resources. In this respect, remote sensing technology is a reliable tool for deriving timely and fine-scale information on forest inventory attributes. However, to better predict and understand the functioning of the forest, fine-scale measures of tree number and density must be extrapolated to the forest plot or stand levels through upscaling. In this study, we compared and combined three sources of remotely sensed data, including low point density airborne laser scans (ALS), synthetic aperture radar (SAR) and very-high resolution WorldView-2 imagery to upscale the total number of trees to the plot level in a structurally complex eucalypt forest using random forest regression. We used information on number of trees previously derived from high point density ALS as training data for a random forest regressor and field inventory data for validation. Overall, our modelled estimates resulted in significant fits (p

Fresh water stored by on-farm reservoirs (OFRs) is an important component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season and for crop irrigation during the dry season, yet their seasonal and inter-annual variability and downstream impacts are not quantified. Therefore, OFRs' sub-weekly surface area changes are critical to understanding their dynamics and mitigating their downstream impacts. However, prior to the recent increase in satellite imagery availability and improvement in sensors' spatial resolution, monitoring the OFRs' sub-weekly surface area changes across space and time was challenging because OFRs occur in high numbers (i.e. hundreds) and are small water bodies ( 0.95) and small uncertainties (4–8%) when compared to the validation dataset. We found higher uncertainties (5–14%) when adding S1 to the Kalman filter—this is related to the higher uncertainties found for S1 (~20%). The algorithm can assimilate optical and radar satellite data to increase the OFRs' surface area time series cadence allowing us to investigate sub-weekly surface area changes. The algorithm is not sensor-specific, and it accounts for the uncertainties in both the sensors observations and the resulting surface areas, which are key advantages when compared to other algorithms used to combine satellite data. By improving the surface area observations cadence and providing the surface area uncertainties, the approach presented in this study has the potential to enhance water conservation plans by allowing better assessment and management of the OFRs.

Landscape connectivity is increasingly promoted as a conservation tool to combat the negative effects of habitat loss, fragmentation, and climate change. Given its importance as a key conservation strategy, connectivity science is a rapidly growing discipline. However, most landscape connectivity models consider connectivity for only a single snapshot in time, despite the widespread recognition that landscapes and ecological processes are dynamic. In this paper, we discuss the emergence of dynamic connectivity and the importance of including dynamism in connectivity models and assessments. We outline dynamic processes for both structural and functional connectivity at multiple spatiotemporal scales and provide examples of modeling approaches at each of these scales. We highlight the unique challenges that accompany the adoption of dynamic connectivity for conservation management and planning in the context of traditional conservation prioritization approaches. With the increased availability of time series and species movement data, computational capacity, and an expanding number of empirical examples in the literature, incorporating dynamic processes into connectivity models is more feasible than ever. Here, we articulate how dynamism is an intrinsic component of connectivity and integral to the future of connectivity science.

In dynamic dryland regions, accounting for spatiotemporal landscape dynamics is essential to understanding how ecological habitat networks are affected by hydroclimatic variability at regional or sub-continental scales. Here we assess how changes in the distribution and availability of surface water influence potential landscape connectivity for water-dependent organisms by combining graph theory network analysis with a Landsat-derived, seasonally continuous 25-year surface-water time-series. We focused on Australia’s Murray Darling Basin (MDB), a globally significant and ecologically stressed 1 million km 2 semi-arid region recently affected by two unprecedented hydroclimatic extremes: the 1997–2010 Millennium Drought and 2010–2012 La Nina floods. We constructed potential habitat networks for two dispersal abilities using circuit theory resistance distances, and used ‘habitat availability’ graph theory metrics as indicators of regional-scale connectivity. We analysed 792 unique potential habitat networks containing over 6.6 million nodes, making our study one of the largest spatially explicit ecological network analyses yet conducted. Our indicators of connectivity revealed consistently positive but spatially heterogeneous relationships between flooded habitat area and landscape connectivity. Connectivity increased by over two orders of magnitude along the spectrum from severe drought to flood, associated with a transition from connectivity driven by intra-habitat or short-distance dispersal during drought to long-distance dispersal during wet conditions. Reductions in connectivity during drought were lower than expected given equivalent decreases in surface water habitat area, suggesting habitat network structure provides a degree of resistance to dry conditions. By providing insights into the processes driving connectivity during different phases along the drought-flood spectrum, our approach may assist in guiding conservation management aimed at maintaining or improving landscape connectivity within dynamic environments faced with increasing hydroclimatic variability.

Recent studies have developed novel long-term records of surface water (SW) maps on continental and global scales but due to the spatial and temporal resolution constraints of available satellite sensors, they are either of high spatial and low temporal resolution or vice versa. In this study, we address this limitation by exploring two approaches for generating an 8-day series of Landsat resolution (30 m) SW maps for three floodplain sites in south-eastern Australia during the 2010 La Nina Floods. Firstly, we applied a generalized additive regression model (GAM) that empirically relates Landsat-based SW extent to in-situ river flow to then predict additional time steps. Secondly, we used the STARFM and ESTARFM blending algorithms for predicting the Open Water Likelihood at 8-daily intervals and 30 m resolution from Landsat and MODIS imagery. ESTARFM outperformed STARFM and best blending accuracies were achieved in the floodplain site with the slowest changes in inundation extent through time. There was good agreement between the blended and statistically-modeled 8-day SW series and both series provided new and temporally consistent information about changes in inundation extent throughout the flooding cycles. After careful consideration of accuracy limitations and model assumptions, these SW records hold great potential for assimilation into hydrodynamic and hydrological models as well as improved management of terrestrial freshwater ecosystems.

Despite calls for landscape connectivity research to account for spatiotemporal dynamics, studies have overwhelmingly evaluated the importance of habitats for connectivity at single or limited moments in time. Remote sensing time series represent a promising resource for studying connectivity within dynamic ecosystems. However, there is a critical need to assess how static and dynamic landscape connectivity modelling approaches compare for prioritising habitats for conservation within dynamic environments. To assess whether static landscape connectivity analyses can identify similar important areas for connectivity as analyses based on dynamic remotely sensed time series data. We compared degree and betweenness centrality graph theory metric distributions from four static scenarios against equivalent results from a dynamic 25-year remotely sensed surface-water time series. Metrics were compared at multiple spatial aggregation scales across south-eastern Australia’s 1 million km2 semi-arid Murray–Darling Basin and three sub-regions with varying levels of hydroclimatic variability and development. We revealed large differences between static and dynamic connectivity metric distributions that varied by static scenario, region, spatial scale and hydroclimatic conditions. Static and dynamic metrics showed particularly low overlap within unregulated and spatiotemporally variable regions, although similarities increased at coarse aggregation scales. In regions that exhibit high spatiotemporal variability, static connectivity modelling approaches are unlikely to serve as effective surrogates for more data intensive approaches based on dynamic, remotely sensed data. Although this limitation may be moderated by spatially aggregating static connectivity outputs, our results highlight the value of remotely sensed time series for assessing connectivity in dynamic landscapes.

Vegetation response to flooding across large dryland areas such as Australia's Murray Darling Basin (MDB) is not understood synoptically and with locally relevant detail. We filled this knowledge gap by quantifying vegetation dynamics, defined here as greening and browning due to changing chlorophyll content and leaf area index, in response to flooding and rainfall across the floodplains of the entire MDB. We quantified vegetation and flooding dynamics using the same data source, namely 26 years of high resolution, wall-to-wall satellite data, in a top down statistical modeling approach, where we controlled for rainfall. Our time series (1986–2011) covered a period of extreme hydroclimatic variability, including the South East Australian Millennium Drought, thus providing a research opportunity to investigate how the relationship between vegetation and flooding changed during wet and dry periods. Our results showed that besides rainfall, flooding plays a key role in driving floodplain vegetation dynamics, yet the role of flooding varied across the MDB floodplains. We quantified a change in the relationship of how vegetation responds to rainfall and flooding with an unprecedented level of spatial detail. The change in the relationships coincided primarily with the onset of the Millennium Drought, yet local and regional differences in the timing of the change did occur, suggesting that the beginning of the Millennium Drought did not impact all floodplain areas at the same time. Our synoptic while locally relevant quantification of the changing response of vegetation to rainfall and flooding is a first step to help underpin Australia's investment into environmental water allocations.

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 semiarid Murray Darling Basin across a 25-yr period (1987-2011). 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.

Periodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long-term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat-derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray-Darling Basin (∼1 million km2). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain-lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain-lakes (r2 > 0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r2 > 0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud-affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r2 > 0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods.

Declining forest health can affect crucial ecosystem functions, such as carbon storage in biomass and soils, the regulation of water regimes, the modulation of regional climate and conservation of biodiversity. Airborne laser scanning (ALS) and imaging spectroscopy (IS) are two potentially complementary remote sensing technologies capable of characterizing and monitoring regional forest health. However, the combined use of ALS and IS data to classify the health of individual trees has not yet been assessed. In this study we propose a new approach utilizing ALS and IS combined to characterize the health of individual trees. Firstly, we applied a recently developed bottom-up individual tree delineation algorithm across a structurally complex floodplain eucalypt forest that has experienced episodes of severe dieback over the past six decades. We further calculated ALS and IS indices for delineated tree crowns and used them as predictor variables in machine learning models. We trained and evaluated an object-oriented random forest classifier against field-measured tree crown dieback and transparency ratios, as indicators of eucalypt tree health and crown density, respectively. Our results showed that dieback levels of individual trees can be classified using ALS and IS with an overall accuracy of 81% and a kappa score of 0.66, while the classification of tree crown transparency levels had an overall accuracy of 70% and a kappa score of 0.5. Returned pulse width, intensity and density related ALS indices were the most important predictors in the tree health classification, as they accounted for > 40% of the variance in the data. At the forest level in terms of dieback, 81.5% of correctly delineated trees were classified as healthy, 12.3% as declining and 6.2% as dying or dead. Dieback occurred primarily in areas that were flooded

On-farm reservoirs (OFRs)—artificial water impoundments that retain water from rainfall and run-off—enable farmers to store water during the wet season to be used for crop irrigation during the dry season. However, monitoring the inter- and intra-annual change of these water bodies remains a challenging task because they are typically small (

The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within eco-hydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

Seasonally continuous long-term information on surface water and flooding extent over subcontinental scales is critical for quantifying spatiotemporal changes in surface water dynamics. We used seasonally continuous Landsat TM/ETM + data and generic random forest-based models to synoptically map the extent and dynamics of surface water and flooding (1986–2011) over the Murray–Darling Basin (MDB). The MDB is a large semi-arid basin with competing demands for water that has recently experienced one of the most severe droughts in the southeast of Australia. We used a stratified random probability sampling design with 500 sample pixels each observed across time to assess the accuracy of the surface water maps. We further developed models to map flooded forest at a riparian site that experienced severe tree dieback. Water indices and bands 5 and 6 were among the top 10 explanatory variables most important for mapping surface water. Surface water extent per season per year showed high inter-annual and seasonal variability, with low extent and variability during the Millennium Drought (1999–2009). Accuracy assessment yielded an overall classification accuracy of 99.9% (± 0.02% standard error) with 87% (± 3%) and 96% (± 2%) producer's and user's accuracy of water, respectively. User's and producer's accuracies of water were higher for Landsat 7 than Landsat 5 data. Both producer's and user's accuracies of water were lower in wet years compared to dry years. The approach presented here can be further developed for global application and is relevant to areas with competing water demands. Quantifying the uncertainty of the accuracy assessment and providing an unbiased accuracy estimate are imperative steps when remotely sensed products are intended to be used for follow on applications.

Full-waveform airborne laser scanning (ALS) is a powerful tool for characterizing and monitoring forest structure over large areas at the individual tree level. Most of the existing ALS-based algorithms for individual tree delineation from the point cloud are top-down, which are accurate for delineating cone-shaped conifers, but have lower delineation accuracies over more structurally complex broad-leaf forests. Therefore, in this study we developed a new bottom-up algorithm for detecting trunks and delineating individual trees with complex shapes, such as eucalypts. Experiments were conducted in the largest river red gum forest in the world, located in the south-east of Australia, that experienced severe dieback over the past six decades. For detection of individual tree trunks, we used a novel approach based on conditional Euclidean distance clustering that takes advantage of spacing between laser returns. Overall, the algorithm developed in our study was able to detect up to 67% of field-measured trees with diameter larger than or equal to 13 cm. By filtering ALS based on the intensity, return number and returned pulse width values, we were able to differentiate between woody and leaf tree components, thus improving the accuracy of tree trunk detections by 5% as compared to non-filtered ALS. The detected trunks were used to seed random walks on graph algorithm for tree crown delineation. The accuracy of tree crown delineation for different ALS point cloud densities was assessed in terms of tree height and crown width and resulted in up to 68% of field-measured trees being correctly delineated. The double increase in point density from ~ 12 points/m2 to ~ 24 points/m2 resulted in tree trunk detection increase of 11% (from 56% to 67%) and percentage of correctly delineated crowns increase of 13% (from 55% to 68%). Our results confirm an algorithm that can be used to accurately delineate individual trees with complex structures (e.g. eucalypts and other broadleaves) and highlight the importance of full-waveform ALS for individual tree delineation.

Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the continent. The phenological cycle peak magnitude and integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over north-eastern Australia and within the MDB predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of productivity) showed positive anomalies of more than two standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition between the El Niño induced decadal droughts to flooding caused by La Niña. The quantified spatial-temporal variability in phenology across Australia in response to climate variability presented here provides important information for land management and climate change studies and applications.

Landscape-scale research quantifying ecological connectivity is required to maintain the viability of populations in dynamic environments increasingly impacted by anthropogenic modification and environmental change. To evaluate how surface water network structure, landscape resistance to movement, and flooding affect the connectivity of amphibian habitats within the Murray–Darling Basin (MDB), a highly modified but ecologically significant region of south-eastern Australia. We evaluated potential connectivity network graphs based on circuit theory, Euclidean and least-cost path distances for two amphibian species with different dispersal abilities, and used graph theory metrics to compare regional- and patch-scale connectivity across a range of flooding scenarios. Circuit theory graphs were more connected than Euclidean and least-cost equivalents in floodplain environments, and less connected in highly modified or semi-arid regions. Habitat networks were highly fragmented for both species, with flooding playing a crucial role in facilitating landscape-scale connectivity. Both formally and informally protected habitats were more likely to form important connectivity “hubs” or “stepping stones” compared to non-protected habitats, and increased in importance with flooding. Surface water network structure and the quality of the intervening landscape matrix combine to affect the connectivity of MDB amphibian habitats in ways which vary spatially and in response to flooding. Our findings highlight the importance of utilising organism-relevant connectivity models which incorporate landscape resistance to movement, and accounting for dynamic landscape-scale processes such as flooding when quantifying connectivity to inform the conservation of dynamic and highly modified environments.

Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN).We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans. We developed a model and derived the Australian Land Surface Phenology Product from time series of satellite images.The product is freely available at http://www.auscover.org.au/ and described in this work.Land surface phenology shows high interannual variability for the dry interior of the continent.Land surface phenology metrics can quantify vegetation response to dry and wet years.

Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2.1561/full, Drought is a global issue that is exacerbated by climate change and increasing anthropogenic water demands. The recent occurrence of drought in California provides an important opportunity to examine drought response across ecosystem classes (forests, shrublands, grasslands, and wetlands), which is essential to understand how climate influences ecosystem structure and function. We quantified ecosystem resistance to drought by comparing changes in satellite-derived estimates of water-use efficiency (WUE = net primary productivity [NPP]/evapotranspiration [ET]) under normal (i.e., baseline) and drought conditions (ΔWUE = WUE2014 − baseline WUE). With this method, areas with increasing WUE under drought conditions are considered more resilient than systems with declining WUE. Baseline WUE varied across California (0.08 to 3.85 g C/mm H2O) and WUE generally increased under severe drought conditions in 2014. Strong correlations between ΔWUE, precipitation, and leaf area index (LAI) indicate that ecosystems with a lower average LAI (i.e., grasslands) also had greater C-uptake rates when water was limiting and higher rates of carbon-uptake efficiency (CUE = NPP/LAI) under drought conditions. We also found that systems with a baseline WUE ≤ 0.4 exhibited a decline in WUE under drought conditions, suggesting that a baseline WUE ≤ 0.4 might be indicative of low drought resistance. Drought severity, precipitation, and WUE were identified as important drivers of shifts in ecosystem classes over the study period. These findings have important implications for understanding climate change effects on primary productivity and C sequestration across ecosystems and how this may influence ecosystem resistance in the future., The authors would like to acknowledge the excellent support provided by the Open Science for Synthesis-2014 training (supported by the National Science Foundation under Grant No. OCI-1216894) at the National Center for Ecological Analysis and Synthesis (NCEAS), a center funded by the University of California, Santa Barbara, and the State of California. The authors would like to thank OSS instructors Nancy Baron, Ben Bolker, Stephanie Hampton, Matthew Jones, Karthik Ram, Mark Schildhauer, and the participants of the OSS training. The authors would also like to thank the Goulden Lab at the University of California Irvines. A. J. Pérez-Luque acknowledges funding received by NCEAS and to MICINN (Spanish Government) for the PTA 2011-6322- I contract; D. P. Drucker acknowledges support from the USAID and the U.S. Department of State through the Sustainable Landscapes Brazil program; and M.G. Tulbure acknowledges funding from the Australian Research Council Early Career Researcher Award (DE140101608).

By influencing water tables of saline aquifers, multiyear dry or wet periods can significantly delay or accelerate dryland salinity, but this effect remains poorly quantified at the large river basin scale. The Gravity and Climate Recovery Experiment (GRACE) satellite measures changes in the total water storage of river systems, providing a unique opportunity for better understanding connections between stream salinity and changes in catchment water storages at the large river basin scale. Here, we quantified the role of GRACE total water storage anomalies (TWSA) in stream salinity variability across Australia’s Murray-Darling Basin (∼1 million km2), while also accounting for streamflow and rainfall. We used the MERRA-2 global land surface model to i) place our findings in the context of the longer-term hydroclimatology (1980-present) and ii) to decompose TWSA into groundwater storage as an alternative driver variable. Multivariate time series regression models (generalized additive mixed models or GAMM) showed that the driver variables could explain 20–50% of the variability in stream salinity across 8 sub-catchments in the Murray Darling Basin. TWSA commonly explained as much variability as streamflow, while groundwater storage and TWSA had very similar explanatory power and rainfall only negligible contributions. The 2000–2009 Millennium Drought and the subsequent La Nina Floods had a predominantly decelerating and accelerating effect on stream salinity respectively and these trends were partially explained by trends in TWSA. Our study illustrates that GRACE can be a useful addition for monitoring and modeling dryland salinity over large river basins.

Crop residue is potentially a major biomass feedstock for bio-based industries. Spatial and interannual variability of crop residue yield potential in relation to climatic variability in average of daily mean temperature and total precipitation during crop growing season at regional scale has not previously been investigated. Crop yield data were used to estimate crop residue yield potential and quantify its spatial and temporal variability across the North Central Region of the USA. A correlation analysis was also conducted to examine the relationship between temporal stability of crop residue yield and climatic variability. Temporal variability in crop residue and climate parameters was quantified by the coefficient of variation (CV). Based on this observational study, the counties in the south- eastern part of the North Central Region were observed to have relatively stable crop residue yield potential and also have a relatively low CV of average of daily mean temperature and total precipitation during the crop growing season. The CV of crop residue yield potential was positively correlated with the CVs of average of daily mean temperature and total precipitation. These findings highlight the influences of climatic variability on the spatial and temporal patterns of crop residue yield potential, and emphasize that these factors should be taken into account when developing regional strategies for sustainable bioenergy production.

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.

The U.S. Renewable Fuel Standard calls for 136 billion liters of renewable fuels production by 2022. Switchgrass (Panicum virgatum L.) has emerged as a leading candidate to be developed as a bioenergy feedstock. To reach biofuel production goals in a sustainable manner, more information is needed to characterize potential production rates of switchgrass. We used switchgrass yield data and general additive models (GAMs) to model lowland and upland switchgrass yield as nonlinear functions of climate and environmental variables. We used the GAMs and a 39-year climate dataset to assess the spatio-temporal variability in switchgrass yield due to climate variables alone. Variables associated with fertilizer application, genetics, precipitation, and management practices were the most important for explaining variability in switchgrass yield. The relationship of switchgrass yield with climate variables was different for upland than lowland cultivars. The spatio-temporal analysis showed that considerable variability in switchgrass yields can occur due to climate variables alone. The highest switchgrass yields with the lowest variability occurred primarily in the Corn Belt region, suggesting that prime cropland regions are the best suited for a constant and high switchgrass biomass yield. Given that much lignocellulosic feedstock production will likely occur in regions with less suitable climates for agriculture, interannual variability in yields should be expected and incorporated into operational planning.

Coastal wetland vegetation along the Great Lakes differs strongly with latitude, but most studies of Great Lakes wetland condition have attempted to exclude the effect of latitude to discern anthropogenic effects on condition. We developed an alternative approach that takes advantage of the strong relationship between latitude and coastal wetland floristic condition. Latitude was significantly correlated with 13 of 37 environmental variables tested, including growing degree days, agriculture, atmospheric deposition, nonpoint-source pollution, and soil texture, which suggests that latitude is a good proxy for several environmental drivers of vegetation. Using data from 64 wetlands along the U.S. coast of Lakes Huron, Michigan, Erie, and Ontario, we developed linear regressions between latitude and two measures of floristic condition, the Floristic Quality Index (FQI, adj. r2= 0.437, p

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.

The invasion and expansion of the non-native Phragmites australis in Great Lakes coastal wetlands is of increasing concern, but quantitative studies of the extent, rate, and causes of invasion have been lacking. Here we revisited 307 plots in 14 wetlands along the Great Lakes coast in 2005 that had previously been sampled for vegetation in 2001–2003. During the 2–4 years between sample events, Phragmites occurred in 101 plots. Genetic analysis revealed that none of the Phragmites samples collected at the 14 wetlands belonged to the native genotype. Decreases in water depth and bare soil area were associated with the greatest increases in Phragmites cover. Phragmites invasion was greater on Lakes Michigan, Huron, and Erie than it was on Lake Ontario, and occurred predominantly on sandy substrates. Soil water concentrations of NO3-N, NH3-N, and soluble reactive P did not differ significantly between plots with and without Phragmites. Monitoring coastal wetlands where water level has dropped and controlling Phragmites at early stages of invasion are essential for maintaining healthy Great Lakes coastal wetlands of high species diversity and wildlife habitat. This becomes important as water levels in the Great Lakes have reached extreme lows and are expected to decline with future climate change.

The wetland complex is the functional ecological unit of the prairie pothole region (PPR) of central North America. Diverse complexes of wetlands contribute high spatial and temporal environmental heterogeneity, productivity, and biodiversity to these glaciated prairie landscapes. Climatewarming simulations using the new model WETLANDSCAPE (WLS) project major reductions in water volume, shortening of hydroperiods, and less-dynamic vegetation for prairie wetland complexes. The WLS model portrays the future PPR as a much less resilient ecosystem: The western PPR will be too dry and the eastern PPR will have too few functional wetlands and nesting habitat to support historic levels of waterfowl and other wetland-dependent species. Maintaining ecosystem goods and services at current levels in a warmer climate will be a major challenge for the conservation community.

Assessment of vegetation is an important part of evaluating wetland condition, but it is complicated by the variety of plant communities that are naturally present in freshwater wetlands. We present an approach to evaluate wetland condition consisting of: (1) a stratified random sample representing the entire range of anthropogenic stress, (2) field data representing a range of water depths within the wetlands sampled, (3) nonmetric multidimensional scaling (MDS) to determine a biological condition gradient across the wetlands sampled, (4) hierarchical clustering to interpret the condition results relative to recognizable plant communities, (5) classification and regression tree (CART) analysis to relate biological condition to natural and anthropogenic environmental drivers, and (6) mapping the results to display their geographic distribution. We applied this approach to plant species data collected at 90 wetlands of the U.S. Great Lakes coast that support a variety of plant communities, reflecting the diverse physical environment and anthropogenic stressors present within the region. Hierarchical cluster analysis yielded eight plant communities at a minimum similarity of 25%. Wetlands that clustered botanically were often geographically clustered as well, even though location was not an input variable in the analysis. The eight vegetation clusters corresponded well with the MDS configuration of the data, in which the first axis was strongly related (R2 = 0.787, P < 0.001) with floristic quality index (FQI) and the second axis was related to the Great Lake of occurrence. CART models using FQI and the first MDS axis as the response variables explained 75% and 82% of the variance in the data, resulting in 6-7 terminal groups spanning the condition gradient. Initial CART splits divided the region based on growing degree-days and cumulative anthropogenic stress; only after making these broad divisions were wetlands distinguished by more local characteristics. Agricultural and urban development variables were important correlates of wetland biological condition, generating optimal or surrogate splits at every split node of the MDS CART model. Our findings provide a means of using vegetation to evaluate a range of wetland condition across a broad and diverse geographic region.

QuickBird multispectral satellite images taken in September 2002 (peak biomass) and April 2003 (pre-growing season) were used to map emergent wetland vegetation communities, particularly invasive Phragmites australis and Typha spp., within a diked wetland at the western end of Lake Erie. An unsupervised classification was performed on a nine-layer image stack consisting of all four spectral bands from both dates plus a September Normalized Difference Vegetation Index image. The resulting eight cover classes distinguished three monodominant genera (Phragmites australis, Typha spp., Nelumbo lutea), three multigenera plant communities (wet meadow, other non persistent emergents, woody vegetation), and two unvegetated cover types (water, bare soil). Field validation at 196 data points yielded an overall classification accuracy of 62%, with producer’s accuracy for the eight individual classes ranging from 41 to 91% and user’s accuracy from 17 to 90%. Three-fourths of areas designated as Phragmites were correctly mapped, but 14% were found to be cattail (Typha) during field validation. Lotus (Nelumbo lutea) beds were accurately mapped on multiseason imagery (producer’s accuracy = 91%); these beds had not yet emerged above water in April, but were fully developed in September. Other types of non persistent vegetation were confused with managed areas in which vegetation had been cut and burned to control invasive Phragmites. Multiseason QuickBird imagery is promising for distinguishing certain wetland plant species, but should be used with caution in highly managed areas where vegetation changes may reflect human alterations rather than phenological change.

Emergent plants can be suitable indicators of anthropogenic stress in coastal wetlands if their responses to natural environmental variation can be parsed from their responses to human activities in and around wetlands. We used hierarchical partitioning to evaluate the independent influence of geomorphology, geography, and anthropogenic stress on common wetland plants of the U.S. Great Lakes coast and developed multi-taxa models indicating wetland condition. A seven-taxon model predicted condition relative to watershed-derived anthropogenic stress, and a four-taxon model predicted condition relative to within-wetland anthropogenic stressors that modified hydrology. The Great Lake on which the wetlands occurred explained an average of about half the variation in species cover, and subdividing the data by lake allowed us to remove that source of variation. We developed lake-specific multi-taxa models for all of the Great Lakes except Lake Ontario, which had no plant species with significant independent effects of anthropogenic stress. Plant responses were both positive (increasing cover with stress) and negative (decreasing cover with stress), and plant taxa incorporated into the lake-specific models differed by Great Lake. The resulting models require information on only a few taxa, rather than all plant species within a wetland, making them easier to implement than existing indicators.

Plant taxa identified in 90 U.S. Great Lakes coastal emergent wetlands were evaluated as indicators of physical environment. Canonical correspondence analysis using the 40 most common taxa showed that water depth and tussock height explained the greatest amount of species-environment interaction among ten environmental factors measured as continuous variables (water depth, tussock height, latitude, longitude, and six ground cover categories). Indicator species analysis was used to identify species-environment interactions with categorical variables of soil type (sand, silt, clay, organic) and hydrogeomorphic type (Open-Coast Wetlands, River-Influenced Wetlands, Protected Wetlands). Of the 169 taxa that occurred in a minimum of four study sites and ten plots, 48 were hydrogeomorphic indicators and 90 were soil indicators. Most indicators of Protected Wetlands were bog and fen species which were also organic soil indicators. Protected Wetlands had significantly greater average coefficient of conservatism (C) values than did Open-Coast Wetlands and River-Influenced Wetlands, but average C values did not differ significantly by soil type. Open-Coast and River-Influenced hydrogeomorphic types tended to have sand or silt soils. Clay soils were found primarily in areas with Quaternary glaciolacustrine deposits or clay-rich tills. A fuller understanding of how the physical environment influences plant species distribution will improve our ability to detect the response of wetland vegetation to anthropogenic activities.

Great Lakes coastal wetlands are subject to water level fluctuations that promote the maintenance of coastal wetlands. Point au Sauble, a Green Bay coastal wetland, was an open water lagoon as of 1999, but became entirely vegetated as Lake Michigan experienced a prolonged period of below-average water levels. Repeat visits in 2001 and 2004 documented a dramatic change in emergent wetland vegetation communities. In 2001 non-native Phragmites and Typha were present but their cover was sparse; in 2004 half of the transect was covered by a 3 m tall, invasive Phragmites and non-native Typha community. Percent similarity between plant species present in 2001 versus 2004 was approximately 19% (Jaccard's coefficient), indicating dramatic changes in species composition that took place in only 3 years. The height of the dominant herbaceous plants and coverage by invasive species were significantly higher in 2004 than they were in 2001. However, floristic quality index and coefficient of conservatism were greater in 2004 than 2001. Cover by plant litter did not differ between 2001 and 2004. The prolonged period of below-average water levels between 1999 and early 2004 exposed unvegetated lagoon bottoms as mud flats, which provided substrate for new plant colonization and created conditions conducive to colonization by invasive taxa. PCR/RFLP analysis revealed that Phragmites from Point au Sauble belongs to the more aggressive, introduced genotype. It displaces native vegetation and is tolerant of a wide range of water depth. Therefore it may disrupt the natural cycles of vegetation replacement that occur under native plant communities in healthy Great Lakes coastal wetlands.

Australia is a continent subject to high rainfall variability, which has major influences on runoff and vegetation dynamics. However, the resulting spatial-temporal pattern of flooding and its influence on riparian vegetation has not been quantified in a spatially explicit way. Here we focused on the floodplains of the entire Murray-Darling Basin (MDB), an area that covers over 1M km2, as a case study. The MDB is the country’s primary agricultural area with scarce water resources subject to competing demands and impacted by climate change and more recently by the Millennium Drought (1999–2009). Riparian vegetation in the MDB floodplain suffered extensive decline providing a dramatic degradation of riparian vegetation. We quantified the spatial-temporal impact of rainfall, temperature and flooding patters on vegetation dynamics at the subcontinental to local scales and across inter to intra-annual time scales based on three decades of Landsat (25k images), Bureau of Meteorology data and one decade of MODIS data. Vegetation response varied in space and time and with vegetation types, densities and location relative to areas frequently flooded. Vegetation degradation trends were observed over riparian forests and woodlands in areas where flooding regimes have changed to less frequent and smaller inundation extents. Conversely, herbaceous vegetation phenology followed primarily a ‘boom’ and ‘bust’ cycle, related to inter-annual rainfall variability. Spatial patters of vegetation degradation changed along the N-S rainfall gradient but flooding regimes and vegetation degradation patterns also varied at finer scale, highlighting the importance of a spatially explicit, internally consistent analysis and setting the stage for investigating further cross-scale relationships. Results are of interest for land and water management decisions. The approach developed here can be applied to other areas globally such as the Nile river basin and Okavango River delta in Africa or the Mekong River Basin in Southeast Asia.

Surface water is a critical resource in semi-arid areas. The Murray-Darling Basin (MDB) of Australia, one of the largest semi-arid basins in the world is aiming to set a worldwide example of how to balance multiple interests (i.e. environment, agriculture and urban use), but has suffered significant water shrinkages during the Millennium Drought (1999-2009), followed by extensive flooding. Baseline information and systematic quantification of surface water (SW) extent and flooding dynamics in space and time are needed for managing SW resources across the basin but are currently lacking. To synoptically quantify changes in SW extent and flooding dynamics over MDB, we used seasonally continuous Landsat TM and ETM+ data (1986 – 2011) and generic machine learning algorithms. We further mapped flooded forest at a riparian forest site that experienced severe tree dieback due to changes in flooding regime. We used a stratified sampling design to assess the accuracy of the SW product across time. Accuracy assessment yielded an overall classification accuracy of 99.94%, with producer’s and user’s accuracy of SW of 85.4% and 97.3%, respectively. Overall accuracy was the same for Landsat 5 and 7 data but user’s and producer’s accuracy of water were higher for Landsat 7 than 5 data and stable over time. Our validated results document a rapid loss in SW bodies. The number, size, and total area of SW showed high seasonal variability with highest numbers in winter and lowest numbers in summer. SW extent per season per year showed high interannual and seasonal variability, with low seasonal variability during the Millennium Drought. Examples of current uses of the new dataset will be presented and include (1) assessing ecosystem response to flooding with implications for environmental water releases, one of the largest investment in environment in Australia; (2) quantifying drivers of SW dynamics (e.g. climate, human activity); (3) quantifying changes in SW dynamics and connectivity for water dependent organisms; (4) assessing the impact of flooding on riparian vegetation health. The approach developed here is globally applicable, relevant to areas with competing water demands (e.g. Okavango River delta, Mekong River Basin). Future work should incorporate Landsat 8 and Sentinel-2 data for continued quantification of SW dynamics.

Detailed information on the spatiotemporal dynamic in surface water bodies is important for quantifying the effects of a drying climate, increased water abstraction and rapid urbanization on wetlands. The Swan Coastal Plain (SCP) with over 1500 wetlands is a global biodiversity hotspot located in the southwest of Western Australia, where more than 70% of the wetlands have been lost since European settlement. SCP is located in an area affected by recent climate change that also experiences rapid urban development and ground water abstraction. Landsat TM and ETM+ imagery from 1999 to 2011 has been used to automatically derive a spatially and temporally explicit time-series of surface water body extent on the SCP. A mapping method based on the Landsat data and a decision tree classification algorithm is described. Two generic classifiers were derived for the Landsat 5 and Landsat 7 data. Several landscape metrics were computed to summarize the intra and interannual patterns of surface water dynamic. Top of the atmosphere (TOA) reflectance of band 5 followed by TOA reflectance of bands 4 and 3 were the explanatory variables most important for mapping surface water bodies. Accuracy assessment yielded an overall classification accuracy of 96%, with 89% producer’s accuracy and 93% user’s accuracy of surface water bodies. The number, mean size, and total area of water bodies showed high seasonal variability with highest numbers in winter and lowest numbers in summer. The number of water bodies in winter increased until 2005 after which a decline can be noted. The lowest numbers occurred in 2010 which coincided with one of the years with the lowest rainfall in the area. Understanding the spatiotemporal dynamic of surface water bodies on the SCP constitutes the basis for understanding the effect of rainfall, water abstraction and urban development on water bodies in a spatially explicit way.

Aquatic ecosystems are under threat from multiple stressors, which vary in distribution and intensity across temporal and spatial scales. Monitoring and assessment of these ecosystems have historically focussed on collection of physical and chemical information and increasingly include associated observations on biological condition. However, ecosystem assessment is often lacking because the scale and quality of biological observations frequently fail to match those available from physical and chemical measurements. The advent of high-performance computing, coupled with new earth observation platforms, has accelerated the adoption of molecular and remote sensing tools in ecosystem assessment. To assess how emerging science and tools can be applied to study multiple stressors on a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was held on 10–12 September 2014 at the Sydney Institute of Marine Sciences, Australia. Here we introduce a conceptual framework for assessing multiple stressors across ecosystems using emerging sources of big data and critique a range of available big-data types that could support models for multiple stressors. We define big data as any set or series of data, which is either so large or complex, it becomes difficult to analyse using traditional data analysis methods.