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At least 289 records · Page 16Linked to original sources
USGS2018

Changes in long-term water quality of Baltimore streams are associated with both gray and green infrastructure

The steadily rising global urban population has placed substantial strain on urban water quality, and this strain is projected to increase for the foreseeable future. Considerable attention has been given to the hydrological and physico-chemical effects of urbanization on stream ecosystems. However, due to the relative infancy of the field of urban ecology, long-term water quality analyses in urban streams are sparse. Using a 15-year stream chemistry monitoring record from Baltimore, MD, USA, we quantified long-term trends in nitrate, phosphate, total nitrogen, total phosphorus, chloride, and sulfate export at several sites along a rural-urban gradient. We found no significant change in solute export at most sites, although we did find specific patterns of interest for certain solutes. For example, nitrogen export declined at the most headwater urban site, while phosphorus export declined at the most downstream urban site. Coupling long-term monitoring with data on gray and green infrastructure management throughout the landscape, we established relationships between solute export at the most downstream urban monitoring site and sanitary sewer overflows (SSOs), best management practice (BMP) implementation, and road salt application rates. Phosphorus export was correlated with BMP implementation in the watershed, whereas nitrogen export was related to SSOs. Despite highly urbanized watersheds, water quality does not appear to be declining at most of these sites, suggesting that current management may have limited further impairment. Results of our study suggest that both gray and green infrastructure are key for maintaining and improving water quality in this highly urbanized watershed.

Maryland↗
USGS2025

Microbial ecology of permafrost soils: Populations, processes, and perspectives

Permafrost microbial research has flourished in the past decades, due in part to improvements in sampling and molecular techniques, but also the increased focus on the permafrost greenhouse gas feedback to climate change and other ecological processes in high latitude and alpine permafrost soils. Permafrost microorganisms are adapted to these extreme environments and remain active at low temperatures and when resources are limited. They are also an important component of global elemental cycles as they regulate organic matter turnover and greenhouse gas production, particularly as permafrost thaws. Here we review the permafrost microbiology literature coupled with an exploration of its historical aspects, with a particular focus on a new understanding advanced by molecular biology techniques. We further identify knowledge gaps and ways forward to improve our understanding of microbial contributions to ecosystem biogeochemistry of permafrost-affected systems.

Permafrost and Periglacial Processes↗
USGS2018

Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones

Due to cultural eutrophication and global climate change, an exponential increase in the number and extent of hypoxic zones in marine and freshwater ecosystems has been observed in the last few decades. Hypoxia, or low dissolved oxygen (DO) concentrations, can produce strong negative ecological impacts and, therefore, is a management concern. We measured biomass and densities of Dreissena in Lake Erie, as well as bottom DO in 2014 using 19 high frequency data loggers distributed throughout the central basin to validate a three-dimensional hydrodynamic-ecological lake model. We found that a deep, offshore hypoxic zone was formed by early August, restricting the Dreissena population to shallow areas of the central basin. Deeper than 20 m, where bottom hypoxia routinely develops, only young of the year mussels were found in small numbers, indicating restricted recruitment and survival of young Dreissena . We suggest that monitoring Dreissena distribution can be an effective tool for mapping the extent and frequency of hypoxia in freshwater. In addition, our results suggest that an anticipated decrease in the spatial extent of hypoxia resulting from nutrient management has the potential to increase the spatial extent of profundal habitat in the central basin available for Dreissena expansion.

Lake Erie↗
USGS2021

Scalability and performance tradeoffs in quantifying relationships between elevation and tidal wetland plant communities

Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data-intensive methods such as kriging performing better than 3 more scalable methods involving post-processing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens , less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, -0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation.

Maryland↗
USGS2016

Climatic and topographical factors affecting the vegetative carbon stock of rangelands in arid and semiarid regions of China

Rangeland systems play an important role in ecological stabilization and the terrestrial carbon cycle in arid and semiarid regions. However, little is known about the vegetative carbon dynamics and climatic and topographical factors that affect vegetative carbon stock in these rangelands. Our goal was to assess vegetative carbon stock by examining meteorological data in conjunction with NDVI (normalized difference vegetation index) time series datasets from 2001–2012. An improved CASA (Carnegie Ames Stanford Approach) model was then applied to simulate the spatiotemporal dynamic variation of vegetative carbon stock, and analyze its response to climatic and topographical factors. We estimated the vegetative carbon stock of rangeland in Gansu province, China to be 4.4× 1014 gC, increasing linearly at an annual rate of 9.8×10 11 gC. The mean vegetative carbon density of the whole rangeland was 136.5 gC m -2 . Vegetative carbon density and total carbon varied temporally and spatially and were highly associated with temperature, precipitation and solar radiation. Vegetative carbon density reached the maximal value on elevation at 2500–3500 m, a slope of >30°and easterly aspect. The effect of precipitation, temperature and solar radiation on the vegetative carbon density of five rangeland types (desert and salinized meadow, steppe, alpine meadow, shrub and tussock, and marginal grassland in the forest) depends on the acquired quantity of water and heat for rangeland plants at all spatial scales. The results of this study provide new evidence for explaining spatiotemporal heterogeneity in vegetative carbon dynamics and responses to global change for rangeland vegetative carbon stock, and offer a theoretical and practical basis for grassland agriculture management in arid and semiarid regions.

Gansu↗
USGS2021

Long-term variation in polar bear body condition and maternal investment relative to a changing environment

In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears ( Ursus maritimus ). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progressive decline in access to prey or an increase in the energetic cost of accessing prey has the potential to adversely affect polar bear population dynamics. We examined long-term (1983–2015) patterns of spring body condition (indexed using residual body mass) and maternal investment (i.e., litter mass of cubs-of-the-year and yearlings; COY and YRL) of polar bears from Alaska’s southern Beaufort Sea to evaluate potential relationships with regional- and circumpolar-scale sea ice conditions and atmospheric patterns. The length of the summer open-water (OW) season (i.e., the period of time the sea ice is mostly absent from the continental shelf) increased at a rate of 18 days decade -1 over the study period. However, the OW season duration was not a strong determinant of spring residual body mass or litter mass. Residual body mass of independent (i.e., subadults and adults) female bears varied relative to age class, reproductive status, and the strength of the prior winter’s Arctic Oscillation (i.e., a circumpolar-scale mode of climate variability driven by long-term atmospheric patterns). Spring residual mass of independent males varied with age class and variation in wind speed (i.e., regional-scale short-term atmospheric patterns) during the winter of the year preceding capture. Over the study period, mean annual body mass of adult females unaccompanied by COY declined by 4 kg/ decade -1 , while no temporal trends were evident in the mean annual body mass of adult females with COY, adult males, and subadults. Litter mass of COY varied relative to capture date, maternal age class and mass, litter size, and year of capture. Litter mass of YRL varied with capture date, maternal age class and mass, litter size, variation in winter wind speed (the year of and year preceding capture), and the strength of the prior winter’s Arctic Oscillation. Mean annual litter mass of COY decreased at a rate of 2.6 kg decade -1 and declined 0.68 kg for every 10 kg reduction in maternal mass. No trend was evident in the mean annual litter mass of yearlings. These findings suggest a nuanced response of the southern Beaufort Sea polar bears to environmental change, where some demographic groups (e.g., adult males and subadults) are presently more resilient than others to changes in the Arctic marine ecosystem.

Alaska↗
USGS2011

Conceptual ecological models to support detection of ecological change on Alaska National Wildlife Refuges

More than 31 million hectares of land are protected and managed in 16 refuges by the U.S. Fish and Wildlife Service (USFWS) in Alaska. The vastness and isolation of Alaskan refuges give rise to relatively intact and complete ecosystems. The potential for these lands to provide habitat for trust species is likely to be altered, however, due to global climate change, which is having dramatic effects at high latitudes. The ability of USFWS to effectively manage these lands in the future will be enhanced by a regional inventory and monitoring program that integrates and supplements monitoring currently being implemented by individual refuges. Conceptual models inform monitoring programs in a number of ways, including summarizing important ecosystem components and processes as well as facilitating communication, discussion and debate about the nature of the system and important management issues. This process can lead to hypotheses regarding future changes, likely results of alternative management actions, identification of monitoring indicators, and ultimately, interpretation of monitoring results. As a first step towards developing a monitoring program, the 16 refuges in Alaska each created a conceptual model of their refuge and the landscape context. Models include prominent ecosystem components, drivers, and processes by which components are linked or altered. The Alaska refuge system also recognizes that designing and implementing monitoring at regional and ecoregional extents has numerous scientific, fiscal, logistical, and political advantages over monitoring conducted exclusively at refuge-specific scales. Broad-scale monitoring is particularly advantageous for examining phenomena such as climate change because effects are best interpreted at broader spatial extents. To enable an ecoregional perspective, a rationale was developed for deriving ecoregional boundaries for four ecoregions (Polar, Interior Alaska, Bering Coast, and North Pacific Coast) from the Unified Ecoregions of Alaska. Ecoregional models were then developed to illustrate resources and processes that operate at spatial scales larger than individual refuges within each ecoregion. Conceptual models also were developed for adjacent marine areas, designated as the North Pacific, Bering Sea, and Beaufort-Chukchi Sea Marine Ecoregions. Although many more conceptual models will be required to support development of a regional monitoring program, these definitions of ecoregions and associated conceptual models are an important foundation.

Alaska↗
USGS2016

Defining the next generation modeling of coastal ecotone dynamics in response to global change

Coastal ecosystems are especially vulnerable to global change; e.g., sea level rise (SLR) and extreme events. Over the past century, global change has resulted in salt-tolerant (halophytic) plant species migrating into upland salt-intolerant (glycophytic) dominated habitats along major rivers and large wetland expanses along the coast. While habitat transitions can be abrupt, modeling the specific drivers of abrupt change between halophytic and glycophytic vegetation is not a simple task. Correlative studies, which dominate the literature, are unlikely to establish ultimate causation for habitat shifts, and do not generate strong predictive capacity for coastal land managers and climate change adaptation exercises. In this paper, we first review possible drivers of ecotone shifts for coastal wetlands, our understanding of which has expanded rapidly in recent years. Any exogenous factor that increases growth or establishment of halophytic species will favor the ecotone boundary moving upslope. However, internal feedbacks between vegetation and the environment, through which vegetation modifies the local microhabitat (e.g., by changing salinity or surface elevation), can either help the system become resilient to future changes or strengthen ecotone migration. Following this idea, we review a succession of models that have provided progressively better insight into the relative importance of internal positive feedbacks versus external environmental factors. We end with developing a theoretical model to show that both abrupt environmental gradients and internal positive feedbacks can generate the sharp ecotonal boundaries that we commonly see, and we demonstrate that the responses to gradual global change (e.g., SLR) can be quite diverse.

Ecological Modelling↗
USGS2015

On formally integrating science and policy: walking the walk

The contribution of science to the development and implementation of policy is typically neither direct nor transparent. In 1995, the U.S. Fish and Wildlife Service (FWS) made a decision that was unprecedented in natural resource management, turning to an unused and unproven decision process to carry out trust responsibilities mandated by an international treaty. The decision process was adopted for the establishment of annual sport hunting regulations for the most economically important duck population in North America, the 6 to 11 million mallards Anas platyrhynchos breeding in the mid-continent region of north-central United States and central Canada. The key idea underlying the adopted decision process was to formally embed within it a scientific process designed to reduce uncertainty (learn) and thus make better decisions in the future. The scientific process entails use of models to develop predictions of competing hypotheses about system response to the selected action at each decision point. These prediction not only are used to select the optimal management action, but also are compared with the subsequent estimates of system state variables, providing evidence for modifying degrees of confidence in, and hence relative influence of, these models at the next decision point. Science and learning in one step are formally and directly incorporated into the next decision, contrasting with the usual ad hoc and indirect use of scientific results in policy development and decision-making. Application of this approach over the last 20 years has led to a substantial reduction in uncertainty, as well as to an increase in transparency and defensibility of annual decisions and a decrease in the contentiousness of the decision process. As resource managers are faced with increased uncertainty associated with various components of global change, this approach provides a roadmap for the future scientific management of natural resources.

Journal of Applied Ecology↗
USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗
USGS2026

How wildlife respond to tropical cyclones: Short-term tactics and long-term impacts

From butterflies to lizards and from sharks to seabirds, wildlife exhibit tactics to survive the impacts of tropical cyclones, also known as hurricanes, cyclones, or typhoons depending on where they occur. Some species seek refuge during the storm by moving, some remain in place and ride it out, and others move longer distances, avoiding the main impacts of the storm altogether. Tropical cyclones can have direct impacts on wildlife (e.g. mortality) but can also have indirect effects by altering resources and habitat, with downstream impacts on abundance and recruitment. Using examples from across taxa and ecosystems, we explore the pathways by which tropical cyclones can influence wildlife populations and communities. We describe tactics demonstrated by wildlife that enable them to survive the immediate impacts of the storm, as well as the longer-term impacts after the storm. We give examples of tropical cyclones as a selective pressure and as a facilitator for the introduction of invasive species. We also describe how tropical cyclones may provide a net benefit to some native species. The ecological and evolutionary impacts of tropical cyclones on wildlife can be complex, as they are often intertwined with concurrent pressures from land-use change, human development, and climate change. As the frequency of intense tropical cyclones is predicted to increase globally, identifying the mechanisms by which wildlife cope with such disturbances can aid in understanding and mitigating the impacts of climate change on wildlife.

Biological Reviews↗
USGS2022

A conceptual framework to integrate biodiversity, ecosystem function, and ecosystem service models

Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections thus may be overly optimistic because they do not account for the role of biodiversity in maintaining ecological functions underpinning their provision. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two model integration pathways. The first uses empirical data on biodiversity-ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented at the global scale. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models that can be applied to more systems and taxa than the first pathway. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance development of policies to meet global sustainability goals.

BioScience↗
USGS2012

Integrated monitoring of ecological conditions in wetland-upland landscapes

Landscapes of interwoven wetlands and uplands offer a rich set of ecosystem goods and services. Managing lands to maximize ecosystem services requires information that distinguishes change caused by local actions from broader-scale shifts in climate, land use, and other forms of global change. Satellite and airborne sensors collect valuable data for this purpose, especially when the data are analyzed along with data collected from ground-based sensors. The U.S. Geological Survey (USGS) is using remote sensing technology in this way as part of the Terrestrial Wetland Global Change Research Network to assess effects of climate change interacting with land-use change and other potential stressors along environmental gradients of wetland-upland landscapes in the United States and Canada.

Fact Sheet↗
USGS2012

Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models

Concern over rapid global changes and the potential for interactions among multiple threats are prompting scientists to combine multiple modelling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land‐use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land‐use change, and altered disturbance regimes on species' extinction risk. Each modelling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land‐use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modelling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long‐run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species ( Acanthomintha ilicifolia , or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long‐run populations.

California↗
USGS2025

Limited directional change in mountaintop plant communities over 19 years in western North America

Plant communities on mountain summits are commonly long-lived, cold-adapted perennials with low dispersal ability. These characteristics in tandem with limited area to track suitable conditions make these mountain communities potentially highly vulnerable to climate change, and indicators of climate change impacts. We investigated temporal changes in plant communities on 29 arid mountain summits across eight study regions in California and Nevada, USA, over 19 years. We analyzed community dynamics in terms of species richness, turnover, gain and loss of functional groups, and relative abundance of functional groups. First, across all summits and regions, we found no change in species richness over time. Second, there was relatively high species turnover (21.7%) between the five-year survey intervals, but turnover was not significantly different from random expectation. Within functional groups, forbs had the greatest proportion of gains and cushions had the greatest proportion of losses. Third, qualitative abundance categories presented a small but consistent signal of decrease in the relative abundance of cushions, graminoids, and shrubs/trees over the study period. Across a broad geographic scale and nearly two decades, community patterns were widely similar, suggesting that climate change has not impacted local colonization or extirpation of mountaintop species in this arid region. These findings support observed differences in response to climate change between temperature-limited and water-limited regions globally, and highlight the lagged and variable nature of high-elevation systems. Our findings fill a major data gap on alpine plant community responses to climate change in the western United States and bolster the importance of long-term ecological monitoring with rapid climate change.

California, Nevada↗
USGS2020

Predicting wildlife distribution patterns in New England USA with expert elicitation techniques

Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species distribution models (SDMs) for harvested wildlife species (n = 10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. The models were in general agreement with the literature and provided effect sizes for variables that shape species occurrence. With the exception of gray fox, models performed well when validated against crowdsourced empirical data. We applied models to rasters (30 × 30 m cells) of the New England region to map each species’ distribution. Average regional occurrence probability was highest for coyote (0.92) and white-tailed deer (0.89) and lowest for gray fox (0.42) and moose (0.52). We then stacked distribution maps of each species to estimate and map focal species richness. Species richness ( s ) varied across New England, with highest average richness in the least developed states of Vermont ( s = 7.47) and Maine (s = 7.32), and lowest average richness in the most developed states of Rhode Island (s = 6.13) and Massachusetts (s = 6.61). Our expert-based approach provided relatively inexpensive, comprehensive information that would have otherwise been difficult to obtain given the spatial extent and range of species being assessed. The results provide valuable information about the current distribution of wildlife species and offer a means of exploring how climate and land-use change may impact wildlife in the future.

Connecticut, Maine, Massachusetts, New Hampshire, ↗
USGS2025

C4 photosynthesis, trait spectra, and the fast-efficient phenotype

It has been 60 years since the discovery of C 4 photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C 4 photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C 4 CO 2 -concentrating mechanism supports a general C 4 plant phenotype capable of fast growth and high resource-use efficiencies. The fast-efficient C 4 phenotype has the potential to operate at high productivity rates, while allowing for less biomass allocation to root production and nutrient acquisition, thereby providing opportunities for the evolution of novel trait covariances and the exploitation of new ecological niches. We propose the placement of the C 4 fast-efficient phenotype near the acquisitive pole of the world-wide leaf economic spectrum, but with a pathway-specific span of trait space, wherein selection shapes both acquisitive and conservative adaptive strategies. A trait-based perspective of C 4 photosynthesis will open new paths to crop improvement, global biogeochemical modeling, the management of invasive species, and the restoration of disturbed ecosystems, particularly in grasslands.

New Phytologist↗
USGS2020

Ecology of urban climates: The need for landscape biophysics in cities

In this chapter we will review and evaluate the climatic and ecological parallels between urbanization and climate change, with an emphasis on temperature and its effects on terrestrial ectotherms, a group of organisms thought to be particularly sensitive to climate change. We will summarize patterns of urban warming and how they relate to global climate change projections, then discuss the potential strengths and limitations of urban climates as a source of insight into biotic responses to climate change and highlight future research directions. Future work will benefit from explicit consideration of the mechanisms by which urbanization and urban warming drive ectotherms’ body temperatures and activity periods. Urban ecosystems may provide a valuable first approximation of the dramatic effects of warming on certain terrestrial systems, and continued advances in urban thermal biology will promote a mechanistic understanding of how organisms use, respond to, and adapt to the unique thermal landscapes of cities.

Book chapter↗