Rapid changes in seasonal avian migrations provide compelling examples of biotic response to climate change. Seasonal waves of land surface phenology and temperature are thought to be primary exogenous cues that migrants use to fine tune migration timing. Exploration of the role that these cues play in regulating migration timing requires better spatial, temporal, and taxonomic sampling than is typically available. We analyze weather surveillance radar (WSR) and eBird citizen science data in an effort to understand the macroscale relationships among migration phenology, seasonal waves of land surface phenology and seasonality of temperature. We demonstrate that both WSR and eBird data provide similar estimates of migration timing at local (within 100 km) and macro scales (>20° of latitude). These measures of avian phenology were positively correlated with spring temperatures and land surface phenology (extended spring index of first leaf date), which followed a south to north wave. Start‐of‐spring, estimated from a remotely sensed index of vegetation greenness, did not show the expected pattern from south to north and appears unlikely to be a useful exogenous migration cue within the study region. Future analyses of WSR and eBird data have the potential to provide high‐resolution phenology data that may be useful in understanding spatiotemporal dynamics of migration systems and the response of these systems to climate change.

Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over time is important for forest management but a challenging task. Relatively large uncertainties still exist in the spatial distribution of forests and forest changes in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR) remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we propose a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transitional region with various climates and landscapes, using the integration of the L-band Advanced Land Observation Satellite (ALOS) PALSAR Fine Beam Dual Polarization (FBD) mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3270 random ground plots collected in 2012 and 2013. Compared with the forest products from Japan Aerospace Exploration Agency (JAXA), National Land Cover Database (NLCD), Oklahoma Ecological Systems Map (OKESM) and Oklahoma Forest Resource Assessment (OKFRA), the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km² in 2010, which was close to the area from OKFRA (40,468 km²), but much larger than those from JAXA (32,403 km²) and NLCD (37,628 km²). We analyzed annual forest cover dynamics, and the results show extensive forest cover loss (2761 km², 6.9% of the total forest area in 2010) and gain (3630 km², 9.0%) in southeast and central Oklahoma, and the total area of forests increased by 684 km² from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be helpful to forest management.

Climate change, interacting with and exacerbating anthropogenic modifications to the landscape, is altering ecosystem structure and function, biodiversity, and species distributions. Among the most visible short‐term impacts are the altered ecological roles of foundation species—those species, native or non‐native—that create locally stable environmental conditions and strongly influence ecosystem services. Understanding the future of these species is crucial for projecting impacts on ecosystem services at both local and regional scales. Here we present foundation species by ecoregion study cases across the US South‐Central Region (Louisiana, New Mexico, Oklahoma, and Texas), including C₄ grasses, mesquite, and northern bobwhite in the Southern Great Plains, mangroves and nutria in coastal Louisiana wetlands, tiger salamanders and sandhill cranes in wetlands of the Southern Great Plains, and post and blackjack oaks and eastern redcedar in the Cross Timbers ecoregion. These case studies explore the impacts of climate change on foundation species and the consequences for ecosystem services, the outlook for climate adaptation efforts, and the sustainability of restoration in these systems. We underscore risks and vulnerabilities that stakeholders should consider when managing or restoring natural resources and conserving ecosystem services in an increasingly extreme and variable climate. We show that past management, through a lack of understanding or implementation of actions, has exacerbated shifts in invasive species, resulting in significant changes in ecosystem structure and function. These changes, interacting with landscape fragmentation and shifting land use and exacerbated by climate change, can result in critical losses of biodiversity. Unfortunately, lack of public understanding may hinder political support for restoration efforts and climate adaptation strategies crucial for the continued supply of traditional ecosystem services. Furthermore, the resulting invaded systems may provide opportunities for income via new ecosystem services valued by society that may reduce support for restoration to historical baselines, thus further shifting management priorities. These priorities should be informed by an understanding of past and ongoing ecological trends in region‐specific situations, such as those we present, to highlight the immediacy of climate change impacts on the environment and society and provide evidence for the critical nature of informed management decisions.

Resource-use decisions affect the ecological and human components of the coupled human and natural system (CHANS), but a critique of some frameworks is that they do not address the complexity and tradeoffs within and between the two systems. Land system architecture (LA) was suggested to account for these tradeoffs at multiple levels/scales. LA and landscape ecology (LE) focus on landscape structure (i.e., composition and configuration of land-use and land-cover change [LULCC]) and the processes (social-ecological) resulting from and shaping LULCC. Drawing on mixed-methods research in the Southern Great Plains, we develop a framework that incorporates LA, LE, and governance theory. Public land and water are commons resources threatened by overuse, degradation, and climate change. Resource use is exacerbated by public land and water policies at the state- and local-levels. Our framework provides a foundation for investigating the mechanisms of land systems science (LSS) couplings across multiple levels/scales to understand how and why governance impacts human LULCC decisions (LA) and how those LULCC patterns influence, and are influenced by, the underlying ecological processes (LE). This framework provides a mechanism for investigating the feedbacks between and among the different system components in a CHANS that subsequently impact future human design decisions.

Cimarron County, Oklahoma and Union County, New Mexico, neighboring counties in the Southern High Plains, are part of a vital agricultural region in the United States. This region experiences extended periods of cyclical drought threatening its ability to produce, creating an incentive for extensive center pivot irrigation (CPI). Center pivots draw from the rapidly depleting High Plains Aquifer System. As a result, the prospect of long-term sustainability for these agricultural communities is questionable. We use Remote Sensing and Geographic Information Systems to quantify growth in land irrigated by CPI between the 1950s and 2014, and key informant interviews to explore local perspectives on the causes and impact of such growth. In Cimarron County, OK, CPI increased by the mid-1980s, and has continually increased since. Results suggest adaptation to drought, a depleting aquifer, high corn prices, and less rigid groundwater regulations contribute to CPI growth. Conversely, CPI in Union County, NM, increased until 2010, and then declined. Results also suggest that drought-related agricultural changes and more aggressive well drilling regulations contribute to this decrease. Nevertheless, in both counties, there is a growing concern over the depleting aquifer, the long-term sustainability of CPI, and the region’s economic future.