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Invasion risks may be influenced either negatively or positively by climate change, depending on the species. These can be predicted with species distribution models, but projections can be strongly affected by the source of the environmental data (climate data source, Global Circulation Models GCM and Shared Socio-economic Pathways SSP). We modelled the distribution of Phelsuma grandis and P. laticauda, two Malagasy reptiles that are spreading globally. We accounted for drivers of spread and establishment using socio-economic factors (e.g., distance from ports) and two climate data sources, i.e., Climatologies at High Resolution for the Earth’s and Land Surface Areas (CHELSA) and Worldclim. We further quantified the degree of agreement in invasion risk models that utilised CHELSA and Worldclim data for current and future conditions. Most areas identified as highly exposed to invasion risks were consistently identified (e.g. in Caribbean and Pacific Islands). However, projected risks differed locally. We also found notable differences in quantitative invasion risk (3% difference in suitability scores for P. laticauda and up to 14% for P. grandis) under current conditions. Despite both species native distributions overlapping substantially, climate change will drive opposite responses on invasion risks by 2070 (decrease for P. grandis, increase for P. laticauda). Overall, projections of future invasion risks were the most affected by climate data source, followed by SSP. Our results highlight that assessments of current and future invasion risks are sensitive to the climate data source, especially in islands. We stress the need to account for multiple climatologies when assessing invasion risks.

Invasion risks may be influenced either negatively or positively by climate change, depending on the species. These can be predicted with species distribution models, but projections can be strongly affected by input environmental data (climate data source, Global Circulation Models and Shared Socio-economic Pathways SSP). We modelled the distribution of Phelsuma grandis and P. laticauda, two Malagasy reptiles that are spreading globally. We accounted for drivers of spread and establishment using socio-economic factors (e.g., distance from ports) and two climate data sources, i.e., Climatologies at High Resolution for the Earth’s and Land Surface Areas (CHELSA) and Worldclim. We further quantified the degree of agreement in invasion risk models that utilised CHELSA and Worldclim data for current and future conditions. Most areas identified as highly exposed to invasion risks were consistently identified (e.g. in Caribbean and Pacific Islands). However, projected risks differed locally. We also found notable differences in quantitative invasion risk (3% difference in suitability scores for P. laticauda and up to 14% for P. grandis) under current conditions. Despite both species native distributions overlap substantially, climate change will drive opposite responses on invasion risks by 2070 (decrease for P. grandis, increase for P. laticauda). Overall, projections of future invasion risks were the most affected by climate data source, followed by SSP. Our results highlight that assessments of current and future invasion risks are sensitive to the climate data source, especially in Islands. We stress the need to account for multiple climatologies when assessing invasion risks.

Aim To investigate whether the frequently advocated climate‐matching species distribution modeling approach could predict the well‐characterized colonization of Florida by the Madagascar giant day gecko Phelsuma grandis. Location Madagascar and Florida, USA. Methods To determine the climatic conditions associated with the native range of P. grandis, we used native‐range presence‐only records and Bioclim climatic data to build a Maxent species distribution model and projected the climatic thresholds of the native range onto Florida. We then built an analogous model using Florida presence‐only data and projected it onto Madagascar. We constructed a third model using native‐range presences for both P. grandis and the closely related parapatric species P. kochi. Results Despite performing well within the native range, our Madagascar Bioclim model failed to identify suitable climatic habitat currently occupied by P. grandis in Florida. The model constructed using Florida presences also failed to reflect the distribution in Madagascar by overpredicting distribution, especially in western areas occupied by P. kochi. The model built using the combined P. kochi/P. grandis dataset modestly improved the prediction of the range of P. grandis in Florida, thereby implying competitive exclusion of P. grandis by P. kochi from habitat within the former's fundamental niche. These findings thus suggest ecological release of P. grandis in Florida. However, because ecological release cannot fully explain the divergent occupied niches of P. grandis in Madagascar versus Florida, our findings also demonstrate some degree of in situ adaptation in Florida. Main conclusions Our models suggest that the discrepancy between the predicted and observed range of P. grandis in Florida is attributable to either in situ adaptation by P. grandis within Florida, or a combination of such in situ adaptation and competition with P. kochi in Madagascar. Our study demonstrates that climate‐matching species distribution models can severely underpredict the establishment risk posed by non‐native herpetofauna., Climate‐matching species distribution models (SDMs) are frequently used to predict biotic invasions. Our manuscript details how both release from native‐range competitors and in situ adaptation within the non‐native range have enabled the day gecko Phelsuma grandis to flourish in Florida under bioclimatic conditions classified as highly unsuitable by an otherwise‐well‐performing climate‐matching SDM. By identifying the large discrepancy between the predicted and observed range of P. grandis in Florida, our study highlights the shortcomings of climate‐matching SDMs as conservation management tools, while also providing valuable empirical evidence of the ecological and evolutionary processes underpinning this discrepancy.

Understanding the sources and sinks of methane (CH4) is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to anthropogenic emissions. To understand CH4 flux from biogenic sources in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover. To determine the gaps in physical infrastructure for developing this network, we need to understand the landscape representativeness of the current infrastructure. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms that inform policy decisions and global climate agreements. Using dissimilarity, multidimensional scaling, and cluster analysis, the US was divided into 10 clusters distributed across temperature and precipitation gradients. We evaluated dissimilarity within each cluster for research sites with active CH4 EC towers to identify gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. Through our analysis using climate, land cover, and location variables, we identified priority areas for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US. Clusters corresponding to Alaska and the Rocky Mountains, which are inherently difficult to capture, are the most poorly represented, and all clusters require a greater representation of vegetation types.

Understanding the sources and sinks of CH4 is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to total anthropogenic emissions. The largest sources of uncertainty in scaling bottom-up CH4 estimates stem from limited ground-based measurements and the misalignment between drivers of CH4 fluxes and current land use classifications. To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and landcover. To determine the gaps in physical infrastructure for developing this network, we need to understand the representativeness of current measurements. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms, informing the remote sensing and modelling communities and policy decisions, all the way to IPCC reports. Using multidimensional scaling and a cluster analysis, the US was divided into 10 clusters that were distributed across temperature and wetness gradients. We evaluated the distance to the medoid condition within each cluster for research sites with EC tower infrastructure to identify the gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. These gaps occurred across all EC flux tower networks and independently managed sites as well as in some environmental clusters. Through our analysis using climate, land cover, and location variables, we have identified priority areas to target for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US.

PREMISE Wetland plants regularly experience physiological stresses resulting from inundation; however, plant responses to the interacting effects of water level and inundation duration are not fully understood. METHODS We conducted a mesocosm experiment on two wetland species, sawgrass (Cladium jamaicense) and muhly grass (Muhlenbergia filipes), that co-dominate many freshwater wetlands in the Florida Everglades. We tracked photosynthesis, respiration, and growth at water levels of -10 (control), 10 (shallow), and 35 cm (deep) with reference to soil surface over 6 months. RESULTS The response of photosynthesis to inundation was nonlinear. Specifically, photosynthetic capacity (Amax ) declined by 25% in sawgrass and by 70% in muhly grass after 1-2 months of inundation. After 4 months, Amax of muhly grass in the deep-water treatment declined to near zero. Inundated sawgrass maintained similar leaf respiration and growth rates as the control, whereas inundated muhly grass suppressed both respiration and growth. At the end of the experiment, sawgrass had similar nonstructural carbohydrate pools in all treatments. By contrast, muhly grass in the deep-water treatment had largely depleted sugar reserves but maintained a similar starch pool as the control, which is critical for post-stress recovery. CONCLUSIONS Overall, the two species exhibited nonlinear and contrasting patterns of carbon uptake and use under inundation stress, which ultimately defines their strategies of surviving regularly flooded habitats. The results suggest that a future scenario with more intensive inundation, due to the water management and climate change, may weaken the dominance of muhly grass in many freshwater wetlands of the Everglades.

Climate change has altered global precipitation patterns and has led to greater variation in hydrological conditions. Wetlands are important globally for their soil carbon storage. Given that wetland carbon processes are primarily driven by hydrology, a comprehensive understanding of the effect of inundation is needed. In this study, we evaluated the effect of water level (WL) and inundation duration (ID) on carbon dioxide (CO2 ) fluxes by analysing a 10-year (2008-2017) eddy covariance dataset from a seasonally inundated freshwater marl prairie in the Everglades National Park. Both gross primary production (GPP) and ecosystem respiration (ER) rates showed declines under inundation. While GPP rates decreased almost linearly as WL and ID increased, ER rates were less responsive to WL increase beyond 30 cm and extended inundation periods. The unequal responses between GPP and ER caused a weaker net ecosystem CO2 sink strength as inundation intensity increased. Eventually, the ecosystem tended to become a net CO2 source on a daily basis when either WL exceeded 46 cm or inundation lasted longer than 7 months. Particularly, with an extended period of high-WLs in 2016 (i.e., WL remained >40 cm for >9 months), the ecosystem became a CO2 source, as opposed to being a sink or neutral for CO2 in other years. Furthermore, the extreme inundation in 2016 was followed by a 4-month postinundation period with lower net ecosystem CO2 uptake compared to other years. Given that inundation plays a key role in controlling ecosystem CO2 balance, we suggest that a future with more intensive inundation caused by climate change or water management activities can weaken the CO2 sink strength of the Everglades freshwater marl prairies and similar wetlands globally, creating a positive feedback to climate change.

How aquatic primary productivity influences the carbon (C) sequestering capacity of wetlands is uncertain. We evaluated the magnitude and variability in aquatic C dynamics and compared them to net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) rates within calcareous freshwater wetlands in Everglades National Park. We continuously recorded 30-min measurements of dissolved oxygen (DO), water level, water temperature (Twater), and photosynthetically active radiation (PAR). These measurements were coupled with ecosystem CO2 fluxes over 5 years (2012–2016) in a long-hydroperiod peat-rich, freshwater marsh and a short-hydroperiod, freshwater marl prairie. Daily net aquatic primary productivity (NAPP) rates indicated both wetlands were generally net heterotrophic. Gross aquatic primary productivity (GAPP) ranged from 0 to − 6.3 g C m−2 day−1 and aquatic respiration (RAq) from 0 to 6.13 g C m−2 day−1. Nonlinear interactions between water level, Twater, and GAPP and RAq resulted in high variability in NAPP that contributed to NEE. Net aquatic primary productivity accounted for 4–5% of the deviance explained in NEE rates. With respect to the flux magnitude, daily NAPP was a greater proportion of daily NEE at the long-hydroperiod site (mean = 95%) compared to the short-hydroperiod site (mean = 64%). Although we have confirmed the significant contribution of NAPP to NEE in both long- and short-hydroperiod freshwater wetlands, the decoupling of the aquatic and ecosystem fluxes could largely depend on emergent vegetation, the carbonate cycle, and the lateral C flux.

The field of ecology in the United States is not fully harnessing the diverse perspectives of the American population. Two major limitations to advancing diversity and inclusion include insufficient awareness of biased attitudes and the lack of large-scale faculty engagement in diversity and inclusion programs. Academic institutions must recognize and value individuals that participate in diversity and inclusion programs. Valuing this work will motivate all ecologists to accept the responsibility for these efforts and not simply assume that the few minorities in their field can do this work.

Author(s): Read, QD; Zarnetske, PL; Record, S; Dahlin, KM; Costanza, JK; Finley, AO; Gaddis, KD; Grady, JM; Hobi, ML; Latimer, AM; Malone, SL; Ollinger, SV; Pau, S; Wilson, AM | Abstract: Aim: We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of habitat with diverse physical features (high geodiversity) associated with ecological and evolutionary mechanisms that maintain high biodiversity. Nonetheless, the relationships between biodiversity and habitat vary with spatial and biological context. In this study, we compare how well habitat geodiversity (spatial variation in abiotic processes and features) and climate explain biodiversity patterns of birds and trees. We also evaluate the consistency of biodiversity–geodiversity relationships across ecoregions. Location: Contiguous USA. Time period: 2007–2016. Taxa studied: Birds and trees. Methods: We quantified geodiversity with remotely sensed data and generated biodiversity maps from the Forest Inventory and Analysis and Breeding Bird Survey datasets. We fitted multivariate regressions to alpha, beta and gamma diversity, accounting for spatial autocorrelation among Nature Conservancy ecoregions and relationships among taxonomic, phylogenetic and functional biodiversity. We fitted models including climate alone (temperature and precipitation), geodiversity alone (topography, soil and geology) and climate plus geodiversity. Results: A combination of geodiversity and climate predictor variables fitted most forms of bird and tree biodiversity with ln10% relative error. Models using geodiversity and climate performed better for local (alpha) and regional (gamma) diversity than for turnover-based (beta) diversity. Among geodiversity predictors, variability of elevation fitted biodiversity best; interestingly, topographically diverse places tended to have higher tree diversity but lower bird diversity. Main conclusions: Although climatic predictors tended to have larger individual effects than geodiversity, adding geodiversity improved climate-only models of biodiversity. Geodiversity was correlated with biodiversity more consistently than with climate across ecoregions, but models tended to have a poor fit in ecoregions held out of the training dataset. Patterns of geodiversity could help to prioritize conservation efforts within ecoregions. However, we need to understand the underlying mechanisms more fully before we can build models transferable across ecoregions.

The eddy covariance (EC) technique has been widely used to measure ecosystem surface energy fluxes. However, with a relatively small representative area (footprint), errors are often introduced when upscaling EC data based on variables derived from large-footprint satellite products. Furthermore, EC measurements often fail to close the energy balance in ecosystems such as wetlands. As an alternative to EC, large aperture scintillometers (LAS) have been used to measure sensible heat fluxes (H) over representative areas comparable to the resolution of large-scale satellite images. However, because LAS measurements are based on semi-empirical simulations, their accuracy needs further evaluation across ecosystems. Changes in hydrology on the land surface significantly alter measurement conditions as well as the processes of energy transfer, and thus, may affect the accuracy of energy measurements from LAS. To address this issue, we compared the LAS-measured H (HLAS) to EC-derived H (HEC) in a seasonally inundated wetland within Everglades National Park. Overall, the HLAS was 7.1 ± 0.3 (SE) % greater than HEC, and the ratio of HLAS to HEC remained similar between the wet season, when the ecosystem was inundated, and the dry season. However, inundation, as a thermal buffer in the system, reduced the magnitude of H in the wet season and resulted in a smaller difference of seasonal H budget estimates between LAS and EC measurements. Because of the homogenous land surface at our site, the discrepancies between HLAS and HEC could not be explained by footprint mismatch. Compared to the energy balance closure calculated with HEC, the closure calculated with HLAS was improved by 6% during the dry season but remained similar during the wet season. Overall, we conclude that LAS can be a reliable approach to measure H in wetland ecosystems and its measurements were stable with seasonal hydrological changes.

The Everglades short-hydroperiod freshwater prairies exhibit strong reductions in CO2 uptake that coincide with inundation, but the underlying basis is not fully understood. To address one of the processes potentially underlying this decline, we measured photosynthetic capacity of the dominant species, sawgrass (Cladium jamaicense) and muhly grass (Muhlenbergia filipes), during wet and dry seasons (2009–2012 and 2016–2017). The measurements in the seasonally inundated prairie were compared to those taken on a nearby rarely inundated levee situated 29 cm above the prairie with similar species composition. During the dry seasons, muhly grass exhibited a much higher photosynthetic capacity (28.5 μmol m−2 s−1) than sawgrass (14.2 μmol m−2 s−1), and no differences were found between the prairie and levee for either species. During the wet seasons when the prairie was inundated, photosynthetic capacity declined substantially (67%) in the prairie for muhly grass while it remained similar across seasons for sawgrass. Analyses of leaf reflectance, chlorophyll fluorescence and leaf nutrient indicated photosystem impairment was the main driver of photosynthetic capacity reduction for muhly grass when the ecosystem is inundated. Our study suggests that declines in photosynthesis of macrophytes, particularly muhly grass, contribute to low wet season productivity in Everglades short-hydroperiod freshwater prairies.

Two common approaches to conserving biodiversity are conserving the actors (species) and conserving the stage (habitat). Many management efforts focus on conserving the actors, but a major challenge to this strategy is uncertainty surrounding how species’ geographic ranges might shift in response to global change, including climate and land use change. The Nature Conservancy has moved to conserving the stage, with the aim of maintaining the processes that generate and support biodiversity. This strategy requires knowing how biodiversity responds to geodiversity—the abiotic features and processes that define the stage. Here we explore how remote sensing illuminates the relationship between biodiversity and geodiversity. We introduce a variety of geodiversity measures and discuss how they can be combined with biodiversity data. We then explore the relationship between biodiversity and geodiversity with tree biodiversity data from the US Forest Inventory and Analysis Program and geodiversity data from the Shuttle Radar Topography Mission as a case study and proof of concept. We find that whereas beta diversity was not well explained by geodiversity, both alpha and gamma diversities were positively related to geodiversity. We also outline the challenges and opportunities of using remote sensing to understand the relationship between biodiversity and geodiversity.

Wildfires in forest ecosystems produce landscape mosaics that include relatively unaffected areas, termed fire refugia. These patches of persistent forest cover can support fire-sensitive species and the biotic legacies important for post-fire forest recovery, yet little is known about their abundance and distribution within fire perimeters. Readily accessible 30-m resolution satellite imagery and derived burn severity products are commonly employed to characterize post-fire landscapes, however, coarse image resolution, generalized burn severity thresholds, and other limitations can constrain accurate representation of fire refugia. This study quantifies the abundance and pattern of fire refugia within 10 fires occurring in ponderosa pine and dry mixed-conifer forests between 2000 and 2003. We developed high-resolution maps of post-fire landscapes using semi-automated, object-based classification of 1-m aerial imagery, conducted imagery- and field-based accuracy assessments, and contrasted these with Landsat-derived burn severity metrics. Fire refugia area within burn perimeters ranged from 20% to 57%. Refugia proportion generally decreased with increasing Landsat-derived burn severity, but still accounted for 3&ndash, 12% of areas classified as high severity. Patch size ranged from 1-m2 isolated trees to nearly 8000 ha, and median patch size was 0.01 ha&mdash, substantially smaller than a 30-m Landsat pixel. Patch size was negatively related to burn severity, distance to fire refugia from open areas was positively related to burn severity. Finally, optimized thresholds of 30-m post-fire normalized burn ratio (NBR) and relative differenced normalized burn ratio (RdNBR) delineated fire refugia with an accuracy of 77% when validated against the 1-m resolution maps. Estimations of fire refugia abundance based on Landsat-derived burn severity metrics are unlikely to detect small, isolated fire refugia patches. Finer-resolution maps can improve understanding of the distribution of forest legacies and inform post-fire management activities including reforestation and treatments.

Author(s): Zarnetske, Phoebe L; Read, Quentin D; Record, Sydne; Gaddis, Keith D; Pau, Stephanie; Hobi, Martina L; Malone, Sparkle L; Costanza, Jennifer; M Dahlin, Kyla; Latimer, Andrew M; Wilson, Adam M; Grady, John M; Ollinger, Scott V; Finley, Andrew O | Abstract: IssueGeodiversity (i.e., the variation in Earth's abiotic processes and features) has strong effects on biodiversity patterns. However, major gaps remain in our understanding of how relationships between biodiversity and geodiversity vary over space and time. Biodiversity data are globally sparse and concentrated in particular regions. In contrast, many forms of geodiversity can be measured continuously across the globe with satellite remote sensing. Satellite remote sensing directly measures environmental variables with grain sizes as small as tens of metres and can therefore elucidate biodiversity-geodiversity relationships across scales.EvidenceWe show how one important geodiversity variable, elevation, relates to alpha, beta and gamma taxonomic diversity of trees across spatial scales. We use elevation from NASA's Shuttle Radar Topography Mission (SRTM) and c.n16,000 Forest Inventory and Analysis plots to quantify spatial scaling relationships between biodiversity and geodiversity with generalized linear models (for alpha and gamma diversity) and beta regression (for beta diversity) across five spatial grains ranging from 5 to 100nkm. We illustrate different relationships depending on the form of diversity; beta and gamma diversity show the strongest relationship with variation in elevation.ConclusionWith the onset of climate change, it is more important than ever to examine geodiversity for its potential to foster biodiversity. Widely available satellite remotely sensed geodiversity data offer an important and expanding suite of measurements for understanding and predicting changes in different forms of biodiversity across scales. Interdisciplinary research teams spanning biodiversity, geoscience and remote sensing are well poised to advance understanding of biodiversity-geodiversity relationships across scales and guide the conservation of nature.

Many recent wildfires in ponderosa pine ( Pinus ponderosa Lawson & C. Lawson) – dominated forests of the western United States have burned more severely than historical ones, generating concern about forest resilience. This concern stems from uncertainty about the ability of ponderosa pine and other co-occurring conifers to regenerate in areas where no surviving trees remain. We collected post-fire conifer regeneration and other data within and surrounding five 11–18 year-old Colorado Front Range wildfires to examine whether high severity burn areas (i.e., areas without surviving trees) are regenerating, and how regeneration density is related to abiotic and biotic factors such as distance from surviving forest, elevation, and aspect. We found that some conifer regeneration has occurred in high severity burn areas (mean and median of 118 and 0 stems ha −1 , respectively), but at densities that were considerably lower than those in unburned and in low to moderate severity burn areas. Generalized estimating equation analyses revealed that distance from surviving forest was the most important predictor of conifer regeneration in high severity burn areas, with regeneration declining as distance from surviving forest increased; estimates of conifer regeneration were 211 stems ha −1 immediately adjacent to surviving forest but only 10 stems ha −1 200 m from surviving forest. These analyses also revealed that conifer regeneration densities declined as elevation decreased. Regression tree analyses likewise showed that distance from surviving forest and elevation were important predictors of conifer regeneration in high severity burn areas; within 50 m of surviving forest mean (median) regeneration was 150 (0) stems ha −1 at elevations ⩽2490 m and 1120 (1000) stems ha −1 at elevations >2490 m, but at distances ⩾50 m from surviving forest mean (median) regeneration was only 49 (0) stems ha −1 , regardless of elevation. Applying regression tree results spatially to the 2002 Hayman Fire, Colorado’s largest and most severe known wildfire, we found that 70% of the area without surviving forest exceeded this 50 m threshold. These patterns of conifer regeneration suggest that Colorado Front Range ponderosa pine – dominated forests may not be resilient to high severity wildfire, particularly where surviving forest is not in close proximity. We recommend that land managers consider planting conifers within the interiors of large high severity burn patches, as well as implementing treatments to reduce the risk of uncharacteristic high severity wildfire in unburned forests, where maintaining a forested condition is desired.

We analyzed the ecosystem effects of low-temperature events (

Water scarcity is becoming a major constraint for maize cultivation and increasing maize yield globally. Water-saving irrigation methods are required to increase crop water productivity (CWP) without reducing grain yield and nutrient uptake. We evaluated the effects of different methods and levels of irrigation on crop dry matter, grain yield, nutrient uptake and CWP of maize (cv. ‘BARI Hybrid Bhutta-9′) in field condition in a sub-tropical environment in Bangladesh. The experiment was laid out in a nested plot design with three replications during 2014−15 and 2015−16. The treatments were three irrigation levels (I1: 100 % field capacity, FC; I2: 80 % FC; I3: 60 % FC) and three irrigation methods (AFI: alternate furrow irrigation; SFFI: skip-fixed furrow irrigation; and TFI: traditional furrow irrigation). Results indicate that both the irrigation method and level had significant effects on dry matter, grain yield, nutrient uptake, and CWP. The AFI technique maintained similar grain yield (8.1 t ha−1) to TFI but reduced irrigation water by 37 % for irrigation applied at 100 % of FC. Both AFI and TFI had significantly higher (p ≤ 0.05) grain yield compared to SFFI at the irrigation level of I2. The interactive effect of irrigation level and method also had a significant effect (p ≤ 0.05) on maize yield. The uptakes of macronutrients (N, P, K, S, Ca, and Mg) and micronutrients (B, Zn, Fe, and Mn) by maize grain were not significantly different between AFI and TFI under I1. Irrigation level was the main driver for determining the patterns in grain yield but irrigation method controlled the patterns in CWP. The AFI technique resulted in higher CWP compared to TFI or SFFI. Results demonstrate that AFI is an effective water-saving technique, which can increase the CWP without a significant reduction in grain yield and nutrients uptake by maize grain. The AFI method in conjunction with a reduced amount of irrigation water can be adapted in the sub-tropical climates of South Asia where maize production in the dry season is heavily dependent on repeated irrigation with limited water supplies.

Drought has many consequences in the tidally dominated Spartina sp. salt marshes of the southeastern US; including major dieback events, changes in sediment chemistry and obvious changes in the landscape. These coastal systems tend to be highly productive, yet many salt marshes are also nitrogen limited and depend on plant associated diazotrophs as their source of ‘new’ nitrogen. A 4-year study was conducted to investigate the structure and composition of the rhizosphere diazotroph assemblages associated with 5 distinct plant zones in one such salt marsh. A period of greatly restricted tidal inundation and precipitation, as well as two periods of drought (June–July 2004, and May 2007) occurred during the study. DGGE of nifH PCR amplicons from rhizosphere samples, Principal Components Analysis of the resulting banding patterns, and unconstrained ordination analysis of taxonomic data and environmental parameters were conducted. Diazotroph assemblages were organized into 5 distinct groups (R2 = 0.41, p value < 0.001) whose presence varied with the environmental conditions of the marsh. Diazotroph assemblage group detection differed during and after the drought event, indicating that persistent diazotrophs maintained populations that provided reduced supplies of new nitrogen for vegetation during the periods of drought.

Like many other high elevation alpine tree species, Rocky Mountain bristlecone pine (Pinus aristata Engelm.) may be particularly vulnerable to climate change. To evaluate its potential vulnerability to shifts in climate, we defined the suitable climate space for each of four genetic lineages of bristlecone pine and for other subalpine tree species in close proximity to bristlecone pine forests. Measuring changes in the suitable climate space for lineage groups is an important step beyond models that assume species are genetically homogenous. The suitable climate space for bristlecone pine in the year 2090 is projected to decline by 74% and the proportional distribution of suitable climate space for genetic lineages shifts toward those associated with warmer and wetter conditions. The 2090 climate space for bristlecone pine exhibits a bimodal distribution along an elevation gradient, presumably due to the persistence of the climate space in the Southern Rocky Mountains and exclusion at mid-elevations by conditions that favor the climate space of other species. These shifts have implications for changes in fire regimes, vulnerability to pest and pathogens, and altered carbon dynamics across the southern Rockies, which may reduce the likelihood of bristlecone pine trees achieving exceptional longevity in the future. The persistence and expansion of climate space for southern bristlecone pine genetic lineage groups in 2090 suggests that these sources may be the least vulnerable in the future. While these lineages may be more likely to persist and therefore present opportunities for proactive management (e.g., assisted migration) to maintain subalpine forest ecosystem services in a warmer world, our findings also imply heighted conservation concern for vulnerable northern lineages facing range contractions.

The increased need for water and food security requires the development of new approaches to save water through irrigation management strategies, particularly for center pivot irrigation. To do so entails monitoring of the dynamic variation in wind drift and evaporation losses (WDELs) of irrigation systems under different weather conditions and for relatively long time periods. The historical catch can method has limited our ability to address this goal. Here, a new and easy-to implement methodology, called the strip test, was developed and validated against the catch can technique. Our results showed strong agreement between the catch can method and the strip test for determining the average water application efficiency (WAE ≈ 1-WDEL). Because the strip test method was measured for shorter intervals compared to the catch can method, the variables influencing WAE were able to be compared during each test. WAE had a large variance over time, which was controlled, in part, by wind speed (>4 m s−1). Site-specific characterisation of WDEL is needed to apply this technique. Once applied, it can provide a better understanding of WAE behaviour over the time, and enhance the capability of predicting results for the optimising water use in sprinkler irrigation.

Shaped by the hydrology of the Kissimmee‐Okeechobee‐Everglades watershed, the Florida Everglades is composed of a conglomerate of wetland ecosystems that have varying capacities to sequester and store carbon. Hydrology, which is a product of the region's precipitation and temperature patterns combined with water management policy, drives community composition and productivity. As shifts in both precipitation and air temperature are expected over the next 100 years as a consequence of climate change, CO2 dynamics in the greater Everglades are expected to change. To reduce uncertainties associated with climate change and to explore how projected changes in atmospheric CO2 concentration and climate can alter current CO2 exchange rates in Everglades freshwater marsh ecosystems, we simulated fluxes of carbon among the atmosphere, vegetation, and soil using the DAYCENT model. We explored the effects of low, moderate, and high scenarios for atmospheric CO2 (550, 850, and 950 ppm), mean annual air temperature (+1, +2.5, and +4.2°C) and precipitation (−2, +7, and +14%), as predicted by the IPCC for the year 2100 for the region, on CO2 exchange rates in short‐ and long‐hydroperiod wetland ecosystems. Under 100 years of current climate and atmospheric CO2 concentration, Everglades freshwater marsh ecosystems were estimated to be CO2‐neutral. As atmospheric CO2 concentration increased and under climate change projections, there were slight shifts in the start and length of the wet season (−1 to +7 days) and a small enhancement in the sink capacity (by −169 to −573 g C m−2 century−1) occurred at both short‐ and long‐ hydroperiod ecosystems compared to CO2 dynamics under the current climate regime. Over 100 years, rising temperatures increased net CO2 exchange rates (+1 to 13 g C m−2 century−1) and shifts in precipitation patterns altered cumulative net carbon uptake by +13 to −46 g C m−2 century−1. While changes in ecosystem structure, species composition, and disturbance regimes were beyond the scope of this research, results do indicate that climate change will produce small changes in CO2 dynamics in Everglades freshwater marsh ecosystems and suggest that the hydrologic regime and oligotrophic conditions of Everglades freshwater marshes lowers the ecosystem sensitivity to climate change.

Forests are becoming increasingly vulnerable to rising tree mortality rates in response to warming and drought. In California, the most severe drought on record occurred from 2012–2016 and high tree mortality rates were observed in response to this prolonged drought. Differences in satellite-derived estimates of water-use efficiency (WUE) under normal (i.e., WUEBASELINE) and drought conditions (ΔWUE = WUE2014 − WUEBASELINE) captured variation in drought resilience, and is used here to understand patterns in tree mortality. Across California forests, a low WUEBASELINE under normal conditions was indicative of a low drought resilience and was associated with increasing tree mortality rates. Forested areas with high drought frequency in recent years (2002–2015) and lower WUEBASELINE were the most vulnerable to high post-drought tree mortality. Post drought tree mortality peaked in 2015 and tree mortality was detected in areas where bark beetles were active. Our results show that spatial and temporal changes in WUE can signal shifts in ecosystem resilience and that water-limited forests are sensitive to temperature- and precipitation-driven drought stress. Considering that forests with low resilience will be poised for dieback in the future if climates continue to feature rising temperatures without compensating increases in precipitation, it is becoming increasingly important that we understand drought vulnerability at the ecosystem level and how it changes over time with climate conditions.

We analyzed energy partitioning in short- and long-hydroperiod freshwater marsh ecosystems in the Florida Everglades by examining energy balance components (eddy covariance derived latent energy (LE) and sensible heat (H) flux). The study period included several wet and dry seasons and variable water levels, allowing us to gain better mechanistic information about the control of and changes in marsh hydroperiods. The annual length of inundation is ~5 months at the short-hydroperiod site (25°26′16.5″N, 80°35′40.68″W), whereas the long-hydroperiod site (25°33′6.72″N, 80°46′57.36″W) is inundated for ~12 months annually due to differences in elevation and exposure to surface flow. In the Everglades, surface fluxes feed back to wet season precipitation and affect the magnitude of seasonal change in water levels through water loss as LE (evapotranspiration (ET)). At both sites, annual precipitation was higher than ET (1304 versus 1008 at the short-hydroperiod site and 1207 versus 1115 mm yr−1 at the long-hydroperiod site), though there were seasonal differences in the ratio of ET:precipitation. Results also show that energy balance closure was within the range found at other wetland sites (60 to 80%) and was lower when sites were inundated (60 to 70%). Patterns in energy partitioning covaried with hydroperiods and climate, suggesting that shifts in any of these components could disrupt current water and biogeochemical cycles throughout the Everglades region. These results suggest that the complex relationships between hydroperiods, energy exchange, and climate are important for creating conditions sufficient to maintain Everglades ecosystems.

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).

Recent studies suggest that coastal ecosystems can bury significantly more C than tropical forests, indicating that continued coastal development and exposure to sea level rise and storms will have global biogeochemical consequences. The Florida Coastal Everglades Long Term Ecological Research (FCE LTER) site provides an excellent subtropical system for examining carbon (C) balance because of its exposure to historical changes in freshwater distribution and sea level rise and its history of significant long-term carbon-cycling studies. FCE LTER scientists used net ecosystem C balance and net ecosystem exchange data to estimate C budgets for riverine mangrove, freshwater marsh, and seagrass meadows, providing insights into the magnitude of C accumulation and lateral aquatic C transport. Rates of net C production in the riverine mangrove forest exceeded those reported for many tropical systems, including terrestrial forests, but there are considerable uncertainties around those estimates due to the high potential for gain and loss of C through aquatic fluxes. C production was approximately balanced between gain and loss in Everglades marshes; however, the contribution of periphyton increases uncertainty in these estimates. Moreover, while the approaches used for these initial estimates were informative, a resolved approach for addressing areas of uncertainty is critically needed for coastal wetland ecosystems. Once resolved, these C balance estimates, in conjunction with an understanding of drivers and key ecosystem feedbacks, can inform cross-system studies of ecosystem response to long-term changes in climate, hydrologic management, and other land use along coastlines.

Hydrology drives the carbon balance of wetlands by controlling the uptake and release of CO2 and CH4 . Longer dry periods in between heavier precipitation events predicted for the Everglades region, may alter the stability of large carbon pools in this wetland's ecosystems. To determine the effects of drought on CO2 fluxes and CH4 emissions, we simulated changes in hydroperiod with three scenarios that differed in the onset rate of drought (gradual, intermediate, and rapid transition into drought) on 18 freshwater wetland monoliths collected from an Everglades short-hydroperiod marsh. Simulated drought, regardless of the onset rate, resulted in higher net CO2 losses net ecosystem exchange (NEE) over the 22-week manipulation. Drought caused extensive vegetation dieback, increased ecosystem respiration (Reco ), and reduced carbon uptake gross ecosystem exchange (GEE). Photosynthetic potential measured by reflective indices (photochemical reflectance index, water index, normalized phaeophytinization index, and the normalized difference vegetation index) indicated that water stress limited GEE and inhibited Reco . As a result of drought-induced dieback, NEE did not offset methane production during periods of inundation. The average ratio of net CH4 to NEE over the study period was 0.06, surpassing the 100-year greenhouse warming compensation point for CH4 (0.04). Drought-induced diebacks of sawgrass (C3 ) led to the establishment of the invasive species torpedograss (C4 ) when water was resupplied. These changes in the structure and function indicate that freshwater marsh ecosystems can become a net source of CO2 and CH4 to the atmosphere, even following an extended drought. Future changes in precipitation patterns and drought occurrence/duration can change the carbon storage capacity of freshwater marshes from sinks to sources of carbon to the atmosphere. Therefore, climate change will impact the carbon storage capacity of freshwater marshes by influencing water availability and the potential for positive feedbacks on radiative forcing.

Coastal communities in the southeast United States have regularly experienced severe hurricane impacts. To better facilitate recovery efforts in these communities following natural disasters, state and federal agencies must respond quickly with information regarding the extent and severity of hurricane damage and the amount of tree debris volume. A tool was developed to detect downed trees and debris volume to better aid disaster response efforts and tree debris removal. The tool estimates downed tree debris volume in hurricane affected urban areas using a Leica Airborne Digital Sensor (ADS40) and very high resolution digital images. The tool employs a Sobel edge detection algorithm combined with spectral information based on color filtering using 15 different statistical combinations of spectral bands. The algorithm identified downed tree edges based on contrasts between tree stems, grass, and asphalt and color filtering was then used to establish threshold values. Colors outside these threshold values were replaced and excluded from the detection processes. Results were overlaid and an “edge line” was placed where lines or edges from longer consecutive segments and color values within the threshold were met. Where two lines were paired within a very short distance in the scene a polygon was drawn automatically and, in doing so, downed tree stems were detected. Tree stem diameter–volume bulking factors were used to estimate post-hurricane tree debris volumes. Images following Hurricane Ivan in 2005 and Hurricane Ike in 2008 were used to assess the error of the tool by comparing downed tree counts and subsequent debris volume estimates with post-hurricane photo-interpreted downed tree counts and actual field measured estimates of downed tree debris volume. The errors associated with the use of the tool and potential applications are also presented., JRC.H.5-Land Resources Management

The southern United States is susceptible to wildfire, from its climate, growing seasons, lightning frequency, and decades of fire suppression. With much known about wildfire's biophysical risks, less is understood about sociodemographic obstacles, including race, income, and education. Blacks in the rural southeastern United States are typically among the most marginalized Americans and least likely to have sufficient knowledge about resource protection. Because disaster preparedness has been shown to vary by sociodemographic status and race, this study focused on race and wildfire prevention by rural forestland owners in north-central Florida. Results show that while Whites were more aware of existing resources, they were less likely to incorporate preventative measures. In contrast, Blacks earned lower incomes and utilized their land less, but were more likely to manage and live on their land. We conclude that wildfire mitigation programs may be more effective when they work to connect absentee rural l...

article i nfo The southeastern U.S. is one of the more wildland fire prone areas of the country and also contains some of the poorest or most socially vulnerable rural communities. Our project addresses wildland fire risk in this part of the U.S and its intersection with social vulnerability. We examine spatial association between high wildland fire prone areas which also rank high in social vulnerability ("hot spots") for Alabama, Arkansas, Florida, Georgia, Mississippi, and South Carolina. We also look at the proximity of hot spots to wildland fire mitigation programs. We hypothesize that hot spots are less likely than high wildland fire risk/low social vulnerability communities to engage with mitigation programs (e.g., Community Wildfire Protection Plans or Firewise Communities). To assess our hypothesis, we examined mean distances between: 1) hot spots and mitigation programs and 2) high wildland fire risk/low social vulnerability communities and mitigation programs. Overall, results show longer mean distances from hot spots to mitigation programs, compared to distances for high wildland fire risk/low social vulnerability communities. This finding provides support for our hypothesis and suggests that poorer communities in the southeast with high wildland fire risk may be at a greater disadvantage than more affluent, high fire risk communities in these states. Published by Elsevier B.V.

Submerged and emergent wetland plant communities are evaluated for their response to global climate change (GCC), focusing on seagrasses, submerged freshwater plants, tidal marsh plants, freshwater marsh plants and mangroves. Similarities and differences are assessed in plant community responses to temperature increase, CO2 increase, greater UV-B exposure, sea level rise and other expected environmental alterations associated with GCC. Responses to most climate change variables are more similar within submerged plant communities, marine or freshwater, than between submerged vs. emergent plant communities. The submerged plants are most affected by temperature increases and indirect impacts on water clarity. Emergent plant communities (marshes and mangroves) respond most directly to climate change related hydrological alterations. Wetland plant communities overall appear to be adversely impacted by all climate change variables, with the exception of increased CO2 in the atmosphere and the oceans, which in most cases increases photosynthesis. Effects of GCC on all these communities have already been seen with many others predicted, including: shifts in species composition, shifts in range and distribution, and declines in plant species richness. Other effects are associated with specific community types, e.g., salt marsh habitat lost to mangrove incursion, and decreases in submerged macrophyte coverage in lakes and estuaries, exacerbated by eutrophication. Sea level rise poses threats to all aquatic plant community types in the vicinity of the oceans, and changes in weather patterns and salinity will affect many. Overall, losses are likely in all these wetland plant communities yet their species can adapt to GCC to some degree if well managed and protected.