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At least 1,711 records · Page 95Linked to original sources
USGS2021

Time marches on, but do the causal pathways driving instream habitat and biology remain consistent?

Stream ecosystems are complex networks of interacting terrestrial and aquatic drivers. To untangle these ecological networks, efforts evaluating the direct and indirect effects of landscape, climate, and instream predictors on biological condition through time are needed. We used structural equation modeling and leveraged a stream survey program to identify and compare important predictors driving condition of benthic macroinvertebrate and fish assemblages. We used data resampled 14 years apart at 252 locations across Maryland, USA. Sample locations covered a wide range of conditions that varied spatiotemporally. Overall, the relationship directions were consistent between sample periods, but their relative strength varied temporally. For benthic macroinvertebrates, we found that the total effect of natural landscape ( e.g ., elevation, longitude, latitude, geology) and land use ( i.e ., forest, development, agriculture) predictors was 1.4 and 1.5 times greater in the late 2010s compared to the 2000s. Moreover, the total effect of water quality ( e.g ., total nitrogen and conductivity) and habitat ( e.g ., embeddedness, riffle quality) was 1.2 and 4.8 times lower in the 2010s, respectively. For fish assemblage condition, the total effect of land use-land cover predictors was 2.3 times greater in the 2010s compared to the 2000s, while the total effect of local habitat was 1.4 times lower in the 2010s, respectively. As expected, we found biological assemblages in catchments with more agriculture and urban development were generally comprised of tolerant, generalist species, while assemblages in catchments with greater forest cover had more-specialized, less-tolerant species ( e.g., Ephemeroptera, Plecoptera, and Trichoptera taxa, clingers, benthic and lithophilic spawning fishes). Changes in the relative importance of landscape and land-use predictors suggest other correlated, yet unmeasured, proximal factors became more important over time. By untangling these ecological networks, stakeholders can gain a better understanding of the spatiotemporal relationships driving biological condition to implement management practices aimed at improving stream condition.

Maryland↗
USGS2024

Adaptation and Response in Drylands (ARID): Community insights for scoping a NASA terrestrial ecology field campaign in drylands

Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at-risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi-year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end-user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end-users and scientists. We also summarize insights gained from hundreds of follow-up activities, including from a tribal-engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements.

Earth's Future↗
USGS2013

Great Lakes rivermouths: a primer for managers

Between the North American Great Lakes and their tributaries are the places where the confluence of river and lake waters creates a distinct ecosystem: the rivermouth ecosystem. Human development has often centered around these rivermouths, in part, because they provide a rich array of ecosystem services. Not surprisingly, centuries of intense human activity have led to substantial pressures on, and alterations to, these ecosystems, often diminishing or degrading their ecological functions and associated ecological services. Many Great Lakes rivermouths are the focus of intense restoration efforts. For example, 36 of the active Great Lakes Areas of Concern (AOCs) are rivermouths or areas that include one or more rivermouths. Historically, research of rivermouth ecosystems has been piecemeal, focused on the Great Lakes proper or on the upper reaches of tributaries, with little direct study of the rivermouth itself. Researchers have been divided among disciplines, agencies and institutions; and they often work independently and use disparate venues to communicate their work. Management has also been fragmented with a focus on smaller, localized, sub-habitat units and socio-political or economic elements, rather than system-level consideration. This Primer presents the case for a more holistic approach to rivermouth science and management that can enable restoration of ecosystem services with multiple benefits to humans and the Great Lakes ecosystem. A conceptual model is presented with supporting text that describes the structures and processes common to all rivermouths, substantiating the case for treating these ecosystems as an identifiable class. 1 Ecological services provided by rivermouths and changes in how humans value those services over time are illustrated through case studies of two Great Lakes rivermouths—the St. Louis River and the Maumee River. Specific ecosystem services are identified in italics throughout this Primer and follow definitions described by the Millennium Ecosystem Assessment (Table1). Collectively, this primer synthesizes existing information in a new way that aims to support management of rivermouths as distinct and important ecosystems. The development and management decisions made around rivermouths today will shape the future of these ecosystems, and the human communities within them, well into the future. 1 The information presented in this paper was derived from discussions and draft documents of the Great Lakes Rivermouth Collaboratory. The Great Lakes Rivermouth Collaboratory was established by the U.S. Geological Survey's Great Lakes Science Center (USGS-GLSC) in collaboration with the Great Lakes Commission to engage the Great Lakes scientific community in sharing and documenting knowledge about freshwater rivermouth ecosystems. For more information, see http://www.glc.org/habitat/Rivermouth-Collaboratory.html.

Great Lakes↗
USGS2021

The demographic and ecological factors shaping diversification among rare Astragalus species

Aim Evolutionary radiations are central to the origin and maintenance of biodiversity, yet we rarely understand how they are jointly shaped by demography and ecological opportunity. Astragalus is the largest plant genus in the world and is disproportionately comprised of rare species restricted to narrow geographic and ecological regions. Here, we explored the demographic and ecological mechanisms underlying patterns of diversification in a threatened Astragalus species complex endemic to a small desert region in the western United States. Location Southeast Utah, USA. Methods We used high‐throughput DNA sequencing to infer genetic structure, genetic diversity, and demographic history (i.e., the timing of population divergence, effective population sizes and gene flow) among Astragalus taxa. We performed landscape genetic analyses to quantify the relationships between genetic differentiation, geographic distance, and ecological distance based on bioclimatic and soil variables. Finally, we identified putative adaptive loci that show higher genetic differentiation between taxa than expected based on our inferred neutral demographic model. Results We found evidence of low gene flow between three highly differentiated taxa (currently delineated as A . iselyi , A . sabulosus var. sabulosus and A. sabulosus var. vehiculus ) that rapidly diverged from a small ancestral population near the beginning of the last glacial period. Genomic signatures revealed long‐term effective population sizes are 2–10× larger than recent census sizes, perhaps due to the maintenance of standing genetic variation through seed banks. Consistent with limited dispersal and local adaptation, genome‐wide patterns of differentiation are shaped by geographic distance (isolation‐by‐distance) and climate and soil variation (isolation‐by‐environment). Taxon‐specific adaptation is further supported by uncovering putative adaptive loci. Main Conclusions Our findings suggest that interactions between demography (i.e., dispersal limitations and seeds banks) and ecological opportunity (i.e., spatial and temporal environmental heterogeneity) may promote diversification, endemism, and rarity among closely related Astragalus species and similar plant clades distributed across complex landscapes.

Utah↗
USGS2007

Toward a transport-based analysis of nutrient spiraling and uptake in streams

Nutrient addition experiments are designed to study the cycling of nutrients in stream ecosystems where hydrologic and nonhydrologic processes determine nutrient fate. Because of the importance of hydrologic processes in stream ecosystems, a conceptual model known as nutrient spiraling is frequently employed. A central part of the nutrient spiraling approach is the determination of uptake length ( S w ), the average distance traveled by dissolved nutrients in the water column before uptake. Although the nutrient spiraling concept has been an invaluable tool in stream ecology, the current practice of estimating uptake length from steady‐state nutrient data using linear regression (called here the “ S w approach”) presents a number of limitations. These limitations are identified by comparing the exponential S w equation with analytical solutions of a stream solute transport model. This comparison indicates that (1) S w is an aggregate measure of uptake that does not distinguish between main channel and storage zone processes, (2) S w is an integrated measure of numerous hydrologic and nonhydrologic processes—this process integration may lead to difficulties in interpretation when comparing estimates of S w , and (3) estimates of uptake velocity and areal uptake rate ( v f and U ) based on S w are not independent of system hydrology. Given these findings, a transport‐based approach to nutrient spiraling is presented for steady‐state and time‐series data sets. The transport‐based approach for time‐series data sets is suggested for future research on nutrient uptake as it provides a number of benefits, including the ability to (1) separately quantify main channel and storage zone uptake, (2) quantify specific hydrologic and nonhydrologic processes using various model parameters (process separation), (3) estimate uptake velocities and areal uptake rates that are independent of hydrologic effects, and (4) use shortterm, non‐plateau nutrient additions such that the effects of regeneration and mineralization are minimized. In summary, the transport‐based, time‐series approach provides a means of estimating traditional measures of nutrient uptake ( S w , v f , U ) while providing additional information on the location and magnitude of uptake (main channel versus storage zone). Application of the transport‐based approach to time‐series data from Green Creek, Antarctica, indicates that the bulk of nitrate uptake (~74% to 100%) occurred within the main channel where benthic uptake by algal mats is a likely process. Substantial uptake (~26%) also occurred in the storage zone of one reach, where uptake is attributed to the microbial community.

Limnology and Oceanography: Methods↗
USGS2022

Endangered Cape Sable seaside sparrow ecology: Actions towards recovery through landscape-scale ecosystem restoration

Understanding the ecology of endangered taxa and the factors affecting their population growth and decline is imperative for their recovery. In the southeastern USA, the Everglades wetland ecosystem supports a high diversity of species and communities, including many endemic and imperiled taxa, such as the federally endangered Cape Sable seaside sparrow Ammospiza maritima mirabilis (CSSS). The Everglades, once a completely connected wetland with a slow-moving sheet flow of water, is now compartmentalized into separated wetland units where water distribution is managed year-round. The CSSS is affected by, and at the crux of, many Everglades ecosystem restoration decisions. The CSSS faces conservation challenges, including limited habitat availability, low population numbers, dispersal limitations, and constraints on suitable breeding conditions owing to wetland water levels. Despite these challenges, ecological knowledge of the factors affecting CSSS population numbers in the context of ongoing ecosystem-level restoration can help inform protection of this bird while restoring the Everglades. Existing research shows target hydroperiods between 90 and 210 days, a minimum of 90 consecutive dry days during the breeding season, and non-breeding season fires approximately every 5-10 years may aid in CSSS recovery. There are numerous tools and models to support habitat and water management for the CSSS, and the most recent ecosystem-level water operations plan for the Everglades indicates potential for increased CSSS habitat. Here, we provide a review on the ecology of the CSSS, factors affecting population decline, and ecosystem-level restoration actions that may aid in CSSS recovery.

Dlorida↗
USGS2026

Connecting and coordinating conservation action across landscapes through regional planning processes and syntheses

Modern threats to conservation are often global; however, relevant conservation decisions and actions typically occur within local jurisdictions. The socio-ecological complexity of these problems requires collaboration across large landscapes, diverse community interests, and geopolitical boundaries, yet a lack of interoperability of planning products among decision makers, jurisdictions, and objectives impedes collaboration. Here, we provide a working model for the collaborative, iterative development of a landscape conservation design (LCD) in the Midwest region of the United States, the Midwest Conservation Blueprint, and demonstrate its applicability to the coordination of voluntary conservation actions and investments across jurisdictional boundaries. We used spatial prioritization software to synthesize over 20 datasets to develop a basemap of priority lands and waters for conservation across a diverse set of societal and ecological values (e.g., water quality, biodiversity, recreation). The Midwest Conservation Blueprint is a living plan, updated annually based on improvements in underlying data, understanding of local conditions, and public input. Landscape-scale planning is not designed to replace local knowledge or supersede decision-making authorities. However, by synthesizing existing data that represent diverse socio-ecological values and planning at a broader scale, the Midwest Conservation Blueprint documents where there are shared priorities and opportunities for collaborative landscape conservation.

Midwest↗
USGS2024

Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences

Estimating distributions for cryptic and highly range-restricted species induces unique challenges for species distribution modeling. In particular, bioclimatic covariates that are typically used to model species ranges at regional and continental scales may not show strong variation at scales of 100s and 10s of meters. This limits both the likelihood and usefulness of correlated occurrence to data typically used in distribution models. Here, we present analyses of species distributions, at 100 × 100 m resolution, for a highly range restricted salamander species (Shenandoah salamander, Plethodon shenandoah ) and a closely related congener (red-backed salamander, Plethodon cinereus ). We combined data across multiple survey types, account for seasonal variation in availability of our target species, and control for repeated surveys at locations– all typical challenges in range-scale monitoring datasets. We fit distribution models using generalized additive models that account for spatial covariates as well as unexplained spatial variation and spatial uncertainty. Our model accommodates different survey protocols using offsets and incorporates temporal variation in detection and availability resulting from survey-specific variation in temperature and precipitation. Our spatial random effect was crucial in identifying small-scale differences in the occurrence of each species and provides cell-specific estimates of uncertainty in the density of salamanders across the range. Counts of both species were seen to increase in the 3 days following a precipitation event. However, P. cinereus were observed even in extremely wet conditions, while surface activity of P. shenandoah was associated with a more narrow range. Our results demonstrate how a flexible analytical approach improves estimates of both distribution and uncertainty, and identify key abiotic relationships, even at small spatial scales and when scales of empirical data are mismatched. While our approach is especially valuable for species with small ranges, controlling for spatial autocorrelation, estimating spatial uncertainty, and incorporating survey-specific information in estimates can improve the reliability of distribution models in general.

Ecology and Evolution↗
USGS2014

The effect of call libraries and acoustic filters on the identification of bat echolocation

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

Ecology and Evolution↗
USGS2011

Projecting avian response to linked changes in groundwater and riparian floodplain vegetation along a dryland river: A scenario analysis

Groundwater is a key driver of riparian condition on dryland rivers but is in high demand for municipal, industrial, and agricultural uses. Approaches are needed to guide decisions that balance human water needs while conserving riparian ecosystems. We developed a space‐for‐time substitution model that links groundwater change scenarios implemented within a Decision Support System (DSS) with proportions of floodplain vegetation types and abundances of breeding and migratory birds along the upper San Pedro River, AZ, USA. We investigated nine scenarios ranging from groundwater depletion to recharge. In groundwater decline scenarios, relative proportions of tall‐canopied obligate phreatophytes ( Populus/Salix , cottonwood/willow) on the floodplain progressively decline, and shrubbier species less dependent on permanent water sources (e.g. Tamarix spp., saltcedar) increase. These scenarios result in broad shifts in the composition of the breeding bird community, with canopy‐nesting and water‐obligate birds declining but midstory nesting birds increasing in abundance as groundwater declines. For the most extreme draw‐down scenario where all reaches undergo groundwater declines, models project that only 10% of the upper San Pedro floodplain would be comprised of cottonwood/willow (73% saltcedar and 18% mesquite), and abundances of canopy‐nesting, water‐obligate, and spring migrant birds would decline 48%, 72%, and 40%, respectively. Groundwater recharge scenarios were associated with increases in canopy‐nesting birds particularly given the extreme recharge scenario (all reaches regain shallow water tables and perennial streamflow). Model outputs serve to assess the sensitivity of biotic groups to potential changes in groundwater and thus to rank scenarios based on their expected ecological impacts.

Ecohydrology↗
USGS2020

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses”, as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.

Ecology↗
USGS2009

Decadal-timescale estuarine geomorphic change under future scenarios of climate and sediment supply

Future estuarine geomorphic change, in response to climate change, sea-level rise, and watershed sediment supply, may govern ecological function, navigation, and water quality. We estimated geomorphic changes in Suisun Bay, CA, under four scenarios using a tidal-timescale hydrodynamic/sediment transport model. Computational expense and data needs were reduced using the morphological hydrograph concept and the morphological acceleration factor. The four scenarios included (1) present-day conditions; (2) sea-level rise and freshwater flow changes of 2030; (3) sea-level rise and decreased watershed sediment supply of 2030; and (4) sea-level rise, freshwater flow changes, and decreased watershed sediment supply of 2030. Sea-level rise increased water levels thereby reducing wave-induced bottom shear stress and sediment redistribution during the wind-wave season. Decreased watershed sediment supply reduced net deposition within the estuary, while minor changes in freshwater flow timing and magnitude induced the smallest overall effect. In all future scenarios, net deposition in the entire estuary and in the shallowest areas did not keep pace with sea-level rise, suggesting that intertidal and wetland areas may struggle to maintain elevation. Tidal-timescale simulations using future conditions were also used to infer changes in optical depth: though sea-level rise acts to decrease mean light irradiance, decreased suspended-sediment concentrations increase irradiance, yielding small changes in optical depth. The modeling results also assisted with the development of a dimensionless estuarine geomorphic number representing the ratio of potential sediment import forces to sediment export forces; we found the number to be linearly related to relative geomorphic change in Suisun Bay. The methods implemented here are widely applicable to evaluating future scenarios of estuarine change over decadal timescales. ?? The Author(s) 2009.

Estuaries and Coasts↗
USGS2016

Hierarchical species distribution models

Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.

Current Landscape Ecology Reports↗
USGS2014

A systematic approach towards the identification and protection of vulnerable marine ecosystems

The United Nations General Assembly in 2006 and 2009 adopted resolutions that call for the identification and protection of vulnerable marine ecosystems (VMEs) from significant adverse impacts of bottom fishing. While general criteria have been produced, there are no guidelines or protocols that elaborate on the process from initial identification through to the protection of VMEs. Here, based upon an expert review of existing practices, a 10-step framework is proposed: (1) Comparatively assess potential VME indicator taxa and habitats in a region; (2) determine VME thresholds; (3) consider areas already known for their ecological importance; (4) compile information on the distributions of likely VME taxa and habitats, as well as related environmental data; (5) develop predictive distribution models for VME indicator taxa and habitats; (6) compile known or likely fishing impacts; (7) produce a predicted VME naturalness distribution (areas of low cumulative impacts); (8) identify areas of higher value to user groups; (9) conduct management strategy evaluations to produce trade-off scenarios; (10) review and re-iterate, until spatial management scenarios are developed that fulfil international obligations and regional conservation and management objectives. To date, regional progress has been piecemeal and incremental. The proposed 10-step framework combines these various experiences into a systematic approach.

Marine Science↗
USGS2023

Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use

Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries.

Earth's Future↗
USGS2026

More water, more of the time: Spatial changes in flooding over 83 years in the upper Mississippi River floodplain and relationships with streamgage-derived proxies

The hydrologic regime of the upper Mississippi River (UMR) has become wetter, with greater discharges, longer-lasting high-flow conditions, and seasonal shifts in these patterns over the past several decades. How these changes are expressed spatially as floodplain inundation area, frequency, depth, duration, and timing is not well understood. It is also unclear to what degree spatial patterns of submergence are represented by examining discharge data alone. We assessed changes in floodplain inundation characteristics from 1940 to 2022 in navigation pools 3–10 of the UMR using a geospatial model to simulate daily inundation depths. Inundation characteristics shifted significantly across pools, but the direction and magnitude of change varied by pool and metric. Characteristics summarized at the pool scale correlated with streamgage-derived proxies but the strength of the relationship varied. Within pools, variability in inundation trends highlighted the importance of spatially explicit modeling. Our study demonstrates that changes in discharge over 83 years have manifested across the UMR floodplain in ways that may have consequences for ecological patterns and processes. By mapping hydrologically sensitive areas, we can anticipate which areas may be susceptible to additional shifts in river discharge in a climatically uncertain future.

Iowa, Minnesota, Wiscosnin↗
USGS2003

Species area relationships in mediterranean-climate plant communities

Aim To determine the best-fit model of species–area relationships for Mediterranean-type plant communities and evaluate how community structure affects these species–area models. Location Data were collected from California shrublands and woodlands and compared with literature reports for other Mediterranean-climate regions. Methods The number of species was recorded from 1, 100 and 1000 m 2 nested plots. Best fit to the power model or exponential model was determined by comparing adjusted r 2 values from the least squares regression, pattern of residuals, homoscedasticity across scales, and semi-log slopes at 1–100 m 2 and 100–1000 m 2 . Dominance–diversity curves were tested for fit to the lognormal model, MacArthur's broken stick model, and the geometric and harmonic series. Results Early successional Western Australia and California shrublands represented the extremes and provide an interesting contrast as the exponential model was the best fit for the former, and the power model for the latter, despite similar total species richness. We hypothesize that structural differences in these communities account for the different species–area curves and are tied to patterns of dominance, equitability and life form distribution. Dominance–diversity relationships for Western Australian heathlands exhibited a close fit to MacArthur's broken stick model, indicating more equitable distribution of species. In contrast, Californian shrublands, both postfire and mature stands, were best fit by the geometric model indicating strong dominance and many minor subordinate species. These regions differ in life form distribution, with annuals being a major component of diversity in early successional Californian shrublands although they are largely lacking in mature stands. Both young and old Australian heathlands are dominated by perennials, and annuals are largely absent. Inherent in all of these ecosystems is cyclical disequilibrium caused by periodic fires. The potential for community reassembly is greater in Californian shrublands where only a quarter of the flora resprout, whereas three quarters resprout in Australian heathlands. Other Californian vegetation types sampled include coniferous forests, oak savannas and desert scrub, and demonstrate that different community structures may lead to a similar species–area relationship. Dominance–diversity relationships for coniferous forests closely follow a geometric model whereas associated oak savannas show a close fit to the lognormal model. However, for both communities, species–area curves fit a power model. The primary driver appears to be the presence of annuals. Desert scrub communities illustrate dramatic changes in both species diversity and dominance–diversity relationships in high and low rainfall years, because of the disappearance of annuals in drought years. Main conclusions Species–area curves for immature shrublands in California and the majority of Mediterranean plant communities fit a power function model. Exceptions that fit the exponential model are not because of sampling error or scaling effects, rather structural differences in these communities provide plausible explanations. The exponential species–area model may arise in more than one way. In the highly diverse Australian heathlands it results from a rapid increase in species richness at small scales. In mature California shrublands it results from very depauperate richness at the community scale. In both instances the exponential model is tied to a preponderance of perennials and paucity of annuals. For communities fit by a power model, coefficients z and log c exhibit a number of significant correlations with other diversity parameters, suggesting that they have some predictive value in ecological communities.

Journal of Biogeography↗
USGS2019

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

GIScience and Remote Sensing↗