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USGS2012

A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring

Aim: To develop a novel global spatial framework for the integration and analysis of ecological and environmental data. Location: The global land surface excluding Antarctica. Methods: A broad set of climate-related variables were considered for inclusion in a quantitative model, which partitions geographic space into bioclimate regions. Statistical screening produced a subset of relevant bioclimate variables, which were further compacted into fewer independent dimensions using principal components analysis (PCA). An ISODATA clustering routine was then used to classify the principal components into relatively homogeneous environmental strata. The strata were aggregated into global environmental zones based on the attribute distances between strata to provide structure and support a consistent nomenclature. Results: The global environmental stratification (GEnS) consists of 125 strata, which have been aggregated into 18 global environmental zones. The stratification has a 30 arcsec resolution (equivalent to 0.86 km2 at the equator). Aggregations of the strata were compared with nine existing global, continental and national bioclimate and ecosystem classifications using the Kappa statistic. Values range between 0.54 and 0.72, indicating good agreement in bioclimate and ecosystem patterns between existing maps and the GEnS. Main conclusions: The GEnS provides a robust spatial analytical framework for the aggregation of local observations, identification of gaps in current monitoring efforts and systematic design of complementary and new monitoring and research. The dataset is available for non-commercial use through the GEO portal (http://www.geoportal.org).

Global Ecology and Biogeography↗

USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Open-File Report↗

USGS2023

Blue snowflakes in a warming world: Karner blue butterfly climate change vulnerability synthesis and best practices for adaptation

This report—developed at the request of the United States Fish and Wildlife Service-led Karner Blue Butterfly Recovery Team by Recovery Team members and partners—provides a Karner blue butterfly climate change vulnerability synthesis, explores a range of potential responses, and presents best practices for climate change-informed conservation of the species. The three decades since the Karner blue butterfly’s (Lycaeides [Plebejus] melissa samuelis Nabokov) listing as federally endangered in the United States have seen a diverse and dedicated research and management community coalesce around the species’ recovery. This geographically expansive conservation collaboration has broken new ground for threatened and endangered species recovery. Many Karner blue recovery areas are making steady progress towards recovery goals, but some are not. An extremely hot and dry 2012, perhaps aided by adverse longer-term climate trends such as declining snow cover, extirpated the Karner blue from Indiana Dunes National Park and the surrounding Indiana Dunes Karner Blue Butterfly Recovery Unit—the southernmost recovery unit and once host to one of the species’ largest populations—as well as from northwest Ohio. Because of the fundamental challenge that climate change represents to Karner blue butterfly recovery and of the general need for endangered species conservation plans to better address climate change, the United States Fish and Wildlife Service-led Karner Blue Butterfly Recovery Team tasked a subset of the team to: Explore the species’ climate change sensitivity and adaptive capacity, review ongoing and projected climate change across the Karner blue range and associated uncertainties, and develop and suggest best practices concerning long-term adaptation strategies. Consistent with that mandate, this report synthesizes what is known about Karner blue climate change vulnerability and applies current thinking in climate change adaptation to help foster strategic, long-term, climate change-informed Karner blue recovery and conservation. It develops and explores a range of potential climate change-informed butterfly- and habitat-stewardship responses, using the resist-accept-direct (RAD) framework to foster a broad range of approaches, and provides guidance regarding how they may be pursued. The report also provides suggestions for improving the climate change-exposure component of Karner blue vulnerability assessment.

Natural Resource Report↗

USGS2020

Holocene paleoclimate change in the western US: The importance of chronology in discerning patterns and drivers

Sediment in lakes and meadows forms a powerful archive that can be used to reconstruct environmental change through time. Reconstructions of lake level, of chemical, biological, and hydrological conditions, and of surrounding vegetation provide detailed information about past climate conditions, both locally and regionally. Indeed, most of our current knowledge of centennial- to millennial-scale climate variability in the arid western United States, where information about past hydroclimate is particularly important, comes from such sediment-based reconstructions. The pressing need for robust, precise predictions of future conditions is a significant motivation for paleoclimate science, and current research questions frequently require Holocene reconstructions to be resolved at sub-centennial timescales. Increasingly, regional syntheses seek to identify synoptic-scale patterns similar to those defined from modern observations (seasonal, interannual, multi-decadal, etc.) or to compare with the output of climate model simulations. However, the age control on existing records, especially those more than about 20 years old, is often sufficient only for millennial-scale interpretation. Here we assess the age control for 84 published and unpublished records from lakes and meadows in the Great Basin, California, and desert southwest, and use Bayesian modeling to evaluate the 95% uncertainty ranges for the 42 best-dated records. In the Late Holocene, about half of the 42 records have <400-year mean uncertainty ranges; however, high-precision age control is especially critical for young records, used to develop an accurate understanding of a proxy’s response to known climate variations. In the Middle Holocene, records vary from 400 to >800-year mean uncertainty and records of the Early Holocene have 600- to >1400-year mean uncertainty ranges. We find that the largest control on modeled uncertainties is dating density, with at least 2 dates/kyr being optimal and suggest obtaining “range-finder” dates at the onset of a study to better predict the total number of dates needed for an adequate age model. Such a density avoids a commonly observed phenomenon of significant peaks in uncertainty arising in gaps between age control points. Analysis of the uncertainties associated with proxy shifts reveal that more than half are >400 years. Although such large uncertainties currently prevent sub-centennial interpretations in most cases, increased dating density, strategic use of limited funds (including budgeting for a 2 date/kyr minimum at the proposal stage), construction of age-depth models with Bayesian methods, and critical evaluation of chronological uncertainty will shed light on past climate variability at finer timescales, enhancing our understanding of global and regional drivers of western U.S. climate.

California, Idaho, Nevada, Oregon, Utah↗

USGS2001

Microbial lime-mud production and its relation to climate change

Microbial calcification has been identified as a significant source of carbonate sediment production in modern marine and lacustrine environments around the globe. This process has been linked to the production of modern whitings and large, micritic carbonate deposits throughout the geologic record. Furthermore, carbonate deposits believed to be the result of cyanobacterial and microalgal calcification suggest that the potential exists for long-term preservation of microbial precipitates and storage of carbon dioxide (CO 2 ). Recent research has advanced our understanding of the microbial-calcification mechanism as a photosynthetically driven process. However, little is known of the effects of this process on inorganic carbon cycling or of the effects of changing climate on microbial-calcification mechanisms. Laboratory experiments on microbial cellular physiology demonstrate that cyanobacteria and green algae can utilize different carbon species for metabolism and calcification. Cyanobacterial calcification relies on bicarbonate (HCO 3 – )utilization while green algae use primarily CO 2 . Therefore, depending on which carbonate species (HCO 3 – or CO 2 ) dominates in the ocean or lacustrine environments (a condition ultimately linked to atmospheric partial pressure PCO 2 ), the origin of lime-mud production by cyanobacteria and/or algae may fluctuate through geologic time. Trends of cyanobacteria versus algal dominance in the rock record corroborate this conclusion. These results suggest that relative species abundances of calcareous cyanobacteria and algae in the Phanerozoic may serve as potential proxies for assessing paleoclimatic conditions, including fluctuations in atmospheric PCO 2 .

AAPG Studies in Geology↗

USGS2023

Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems

The loss of ecosystem services due to climate change and coastal development is projected to have significant impacts on local economies and conservation of natural resources. Consequently, there has been an increase in coastal management activities such as living shorelines, oyster reef restoration, marsh restoration, beach and dune nourishment, and revegetation projects. Coastal management decisions are complex and include challenging trade-offs. Decision science offers a useful framework to address such complex problems. Here, we provide a synthesis about how decision science can help to integrate research from multiple disciplines (physical and life sciences) with management of coastal and marine systems. Specifically, we discuss the importance of considering concepts and techniques from ecology, coastal geology, geomorphology, climate science, oceanography, and decision analysis when developing conservation plans for coastal restoration. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecological models. We show how decision science can be used as a framework to combine geomorphological and ecological modeling to help inform management decisions while considering uncertainty about system changes and risk tolerance. We also build on our examples through a review of recently developed techniques for spatial conservation planning for land acquisition decisions and the application of adaptive management for sequential decisions.

Alabama↗

USGS2014

The effects of atmospheric nitrogen deposition on terrestrial and freshwater biodiversity

This chapter reports the findings of a Working Group on how atmospheric nitrogen (N) deposition affects both terrestrial and freshwater biodiversity. Regional and global scale impacts on biodiversity are addressed, together with potential indicators. Key conclusions are that: the rates of loss in biodiversity are greatest at the lowest and initial stages of N deposition increase; changes in species compositions are related to the relative amounts of N, carbon (C) and phosphorus (P) in the plant soil system; enhanced N inputs have implications for C cycling; N deposition is known to be having adverse effects on European and North American vegetation composition; very little is known about tropical ecosystem responses, while tropical ecosystems are major biodiversity hotspots and are increasingly recipients of very high N deposition rates; N deposition alters forest fungi and mycorrhyzal relations with plants; the rapid response of forest fungi and arthropods makes them good indicators of change; predictive tools (models) that address ecosystem scale processes are necessary to address complex drivers and responses, including the integration of N deposition, climate change and land use effects; criteria can be identified for projecting sensitivity of terrestrial and aquatic ecosystems to N deposition. Future research and policy-relevant recommendations are identified.

Book chapter↗

USGS2008

A multi-model framework for simulating wildlife population response to land-use and climate change

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Ecological Modelling↗

USGS2014

Ecological change on California's Channel Islands from the Pleistocene to the Anthropocene

Historical ecology is becoming an important focus in conservation biology and offers a promising tool to help guide ecosystem management. Here, we integrate data from multiple disciplines to illuminate the past, present, and future of biodiversity on California's Channel Islands, an archipelago that has undergone a wide range of land-use and ecological changes. Our analysis spans approximately 20,000 years, from before human occupation and through Native American hunter–gatherers, commercial ranchers and fishers, the US military, and other land managers. We demonstrate how long-term, interdisciplinary research provides insight into conservation decisions, such as setting ecosystem restoration goals, preserving rare and endemic taxa, and reducing the impacts of climate change on natural and cultural resources. We illustrate the importance of historical perspectives for understanding modern patterns and ecological change and present an approach that can be applied generally in conservation management planning.

California↗

USGS2024

Vulnerability of endemic insular mole skinks to sea-level rise

Although coastal islands are home to many endemic species, they are also at risk of inundation from storm surge and sea level rise. Three subspecies of mole skink ( Plestiodon egregius egregius , P. e. insularis , and the Egmont Key Mole Skink known from a single occurrence) occur on a small number of islands off the Gulf Coast of Florida, USA. We used the most recent sea level rise projections and the latest storm surge simulation data to predict impacts to habitat for insular mole skinks in Florida from 2030 to 2150. Our models predicted that in <100 years (by 2100; intermediate sea level rise scenario; ~1.08–1.15 m sea level rise), >78% of preferred habitat for the Florida Keys Mole Skink, >65% of preferred habitat for the Cedar Key Mole Skink, and >36% of preferred habitat for the Egmont Key Mole Skink will be inundated from sea level rise. Storm surge from tropical cyclones presents a more immediate risk to insular mole skink habitat: our models predicted that between 58% and 75% of Florida Keys Mole Skink habitat is at risk of being submerged under an average maximum of between 0.60 (SD = 0.86) and 0.98 (SD = 0.36) m of storm surge water for a category 1 storm, and the amount of habitat predicted to be impacted increases for higher intensity storms. Our models predicted similar trends for Cedar Key and Egmont Key Mole Skink habitat. Given current sea level rise projections, our models predicted that all three subspecies could be extinct by 2140 due to habitat inundation. There remains uncertainty about how species and ecosystems will respond to sea level rise, thus research to fill these gaps could help mitigate the effects of sea level rise in areas most vulnerable to the effects of climate change.

Conservation Science and Practice↗

USGS1997

Operation of a landslide warning system during the California storm sequence of January and February 1993

From 1986 to late December 1995, the U.S. Geological Survey and the National Weather Service operated a landslide warning system for debris flows triggered by intense rainstorms in the San Francisco Bay region. The Landslide Warning System tracked storm systems as they approached the region, determined actual rainfall with a network of radio-telemetered rain gauges, compared the rainfall to thresholds for initi-ation of debris flows, and issued the appropriate public advisories. A series of intense rainstorms during January 1993 created hazards from landslid-ing and flooding over much of California. In the San Francisco Bay region, January rainfall was over 200% of normal, triggering debris flows on natural hillslopes and road cuts across Marin, San Mateo, Alameda, Santa Clara, and Santa Cruz Counties. The warning system issued Flash Flood/Debris Flow Watches during the most intense storms on January 13 and 15,1993. Most debris flows in this area were small and widely scattered, so damage was largely limited to several blocked roadways in mountainous areas. Storm damage was much heavier in southern California, where rainfall amounts were over 350% of normal for January, triggering flash floods and many landslides. This damage prompted inquiries about developing a landslide warning system for southern California. A number of elements for a landslide warning system already exist in southern Cal-ifornia, including quantitative rainfall forecasting and a network of radio-telemetered rain gauges. Regional rainfall thresholds for debris flow initiation, consistent with the climate, topography, and geology of the region, remain to be developed. Such thresholds could probably be developed with a modest investment of research effort and resources.

California↗

USGS1982

Seasonal growth in the bivalve Macoma balthica near the southern limit of its range

Shell-length growth in Macoma balthica from San Francisco Bay, California, as measured on living animals in situ, is highly seasonal despite a mild Mediterranean climate: a long period of near non-growth from May to the following February is followed by a short period of rapid growth between March and May. The rapid-growth period follows the spawning period during January/February and ends as water temperature rises above about 15??C. Despite the shortness of the growth period, M. balthica grows larger at a given age in San Francisco Bay than is recorded elsewhere in the world. Application of a model, developed elsewhere from these same field measurements, shows that (1) measurable growth occurs during the summer/autumn/early winter "nongrowth" period, (2) there is an autumn recruitment, and (3) both spring and autumn recruits combine to form a single "one-year-old" size grouping. None of these features is detectable through growth-ring analysis of field samples, apparently because of indistinct climatic seasons, or through size-frequency histogram analysis because of the combined effects of slow growth and intermittent recruitment. ?? 1982 Estuarine Research Federation.

Estuaries↗

USGS2003

Establishing causality in the decline and deformity of amphibians: The amphibian research and monitoring initiative model

Research to date has indicated that a range of environmental variables such as disease, parasitism, predation, competition, environmental contamination, solar ultraviolet radiation, climate change, or habitat alteration may be responsible for declining amphibian populations and the appearance of deformed organisms, yet in many cases no definitive environmental variable stands out as a causal factor. Multiple Stressors are often present in the habitat, and interactions among these can magnify injury to biota. This raises the possibility that the additive or synergistic impact of these Stressors may be the underlying cause of amphibian declines. Effective management for the restoration of amphibian populations requires the identification of causal factors contributing to their declines. A systematic approach to determine causality is especially important because initial impressions may be misleading or ambiguous. In addition, the evaluation of amphibian populations requires consideration of a broader spatial scale than commonly used in regulatory monitoring. We describe a systematic three-tiered approach to determine causality in amphibian declines and deformities. Tier 1 includes an evaluation of historic databases and extant data and would involve a desktop synopsis of the status of various stressors as well as site visits. Tier 2 studies are iterative, hypothesis driven studies beginning with general tests and continuing with analyses of increasing complexity as certain stressors are identified for further investigation. Tier 3 applies information developed in Tier 2 as predictive indicators of habitats and species at risk over broad landscape scales and provides decision support for the adaptive management of amphibian recovery. This comprehensive, tiered program could provide a mechanistic approach to identifying and addressing specific stressors responsible for amphibian declines across various landscapes.

Conference Paper↗

USGS2016

Evaluating lidar point densities for effective estimation of aboveground biomass

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m 2 , corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m 2 . Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m 2 , and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m 2 , our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.

International Journal of Advanced Remote Sensing a↗

USGS2020

Fish growth rates and lake sulphate explain variation in mercury levels in ninespine stickleback (Pungitius pungitius) on the Arctic Coastal Plain of Alaska

Mercury concentrations in freshwater food webs are governed by complex biogeochemical and ecological interactions that spatially vary and are often mediated by climate. The Arctic Coastal Plain of Alaska (ACP) is a heterogeneous, lake-rich landscape where variability in mercury accumulation is poorly understood. Earlier research indicated that the level of catchment influence on lakes varied spatially on the ACP, and affected mercury accumulation in lake sediments. This work sought to determine drivers of spatial variation in mercury accumulation in lake food webs on the ACP. Three lakes that were a priori identified as “high catchment influence” (Reindeer Camp region) and three lakes that were a priori identified as “low catchment influence” (Atqasuk region) were sampled, and variability in water chemistry, food web ecology, and mercury accumulation was investigated. Among-lake differences in ninespine stickleback ( Pungitius pungitius ) length-adjusted methylmercury concentrations were significantly explained by sulphate concentration in lake water, a tracer of catchment runoff input. This effect was mediated by fish growth, which had no pattern between regions. Together, lake water sulphate concentration and fish age-at-size (proxy for growth) accounted for nearly all of the among-lake variability in length-adjusted methylmercury concentrations in stickleback (R 2 adj = 0.94, p < 0.01). The percentage of total mercury as methylmercury (a proxy for net Hg methylation) was higher in sediments of more autochthonous, “low catchment influence” lakes ( p < 0.05), and in the periphyton of more allochthonous, “high catchment influence” lakes (p < 0.05). The results indicate that dominant sources of primary production (littoral macrophyte/biofilm vs. pelagic phytoplankton) and food web structure (detrital vs. grazing) are regulated by catchment characteristics on the ACP, and that this ultimately influences the amount of methylmercury in the aquatic food web. These results have important implications for predicting future mercury concentrations in fish in lakes where fish growth rates and catchment inputs may change in response to a changing climate.

Alaska↗

USGS2019

The landscape of soil carbon data: Emerging questions, synergies and databases

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model–data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.

Progress in Physical Geography: Earth and Environm↗

USGS2014

Agricultural conversion without external water and nutrient inputs reduces terrestrial vegetation productivity

Driven by global population and standard of living increases, humanity co-opts a growing share of the planet's natural resources resulting in many well-known environmental trade-offs. In this study, we explored the impact of agriculture on a resource fundamental to life on Earth: terrestrial vegetation growth (net primary production; NPP). We demonstrate that agricultural conversion has reduced terrestrial NPP by ~7.0%. Increases in NPP due to agricultural conversion were observed only in areas receiving external inputs (i.e., irrigation and/or fertilization). NPP reductions were found for ~88% of agricultural lands, with the largest reductions observed in areas formerly occupied by tropical forests and savannas (~71% and ~66% reductions, respectively). Without policies that explicitly consider the impact of agricultural conversion on primary production, future demand-driven increases in agricultural output will likely continue to drive net declines in global terrestrial productivity, with potential detrimental consequences for net ecosystem carbon storage and subsequent climate warming.

Geophysical Research Letters↗

USGS2024

Patterns and drivers of cottonwood mortality in the middle Rio Grande, New Mexico, USA

Riparian ecosystems are some of the most valuable and vulnerable on the planet. Riparian tree mortality is increasing in the western United States, where altered streamflows are combining with warming climate. Between 2011 and 2013, one third of an extensive stand of Populus deltoides var. wislizeni (Rio Grande cottonwood) died along the middle Rio Grande on the Pueblo of Santa Ana in New Mexico. Mortality coincided with a severe drought that followed a decade of decreasing streamflow, but it was heterogeneous, with adjacent patches of dead and live trees. The goal of this research was to determine the drivers of mortality to provide insights into future risks of die-off and potential management interventions. We compared tree age, competition, tree-ring widths, sediment particle size and climate influences between live and dead forest patches in a nested plot design. Live and dead trees had similar age, stand density and particle sizes of shallow sediments. Tree-ring widths had the highest correlations with July–September streamflow (1932–2013). All trees had declining ring growth since 1992, coinciding with declining late summer streamflow. An accelerated decline in growth began in 2002, corresponding to recent warmer droughts. Trees that died had lower ring growth 3 years prior to death and in the mid-1900s. Dead trees also had coarser deep sediments 2.4–3.7 m below ground, suggesting that reduced water holding capacity was an important factor for mortality. Water management to increase streamflow during the late summer, especially during times of extended drought, could reduce mortality risk in the face of projected increasingly warm droughts.

New Mexico↗