Rangeland comprises as much as 70% of the Earth's land surface area. Much of this vast space is in very remote areas that are expensive and often impossible to access on the ground. Unmanned Aerial Vehicles (UAVs) have great potential for rangeland management. UAVs have several advantages over satellites and piloted aircraft: they can be deployed quickly and repeatedly; they are less costly and safer than piloted aircraft; they are flexible in terms of flying height and timing of missions; and they can obtain imagery at sub-decimeter resolution. This hyperspatial imagery allows for quantification of plant cover, composition, and structure at multiple spatial scales. Our experiments have shown that this capability, from an off-the-shelf mini-UAV, is directly applicable to operational agency needs for measuring and monitoring. For use by operational agencies to carry out their mandated responsibilities, various requirements must be met: an affordable and reliable platform; a capability for autonomous, low altitude flights; takeoff and landing in small areas surrounded by rugged terrain; and an easily applied data analysis methodology. A number of image processing and orthorectification challenges have been or are currently being addressed, but the potential to depict the land surface commensurate with field data perspectives across broader spatial extents is unrivaled.

Identifying factors that may be responsible for regulating the size of animal populations is a cornerstone in understanding population ecology. The main factors that are thought to influence population size are either resources (bottom‐up), or predation (top‐down), or interspecific competition (parallel). However, there are highly variable and often contradictory results regarding their relative strengths and influence. These varied results are often interpreted as indicating “shifting control” among the three main factors, or a complex, nonlinear relationship among environmental variables, resource availability, predation, and competition. We argue here that there is a “missing link” in our understanding of predator–prey dynamics. We explore whether the landscape‐of‐fear model can help us clarify the inconsistencies and increase our understanding of the roles, extent, and possible interactions of top‐down, bottom‐up, and parallel factors on prey population abundance. We propose two main predictions derived from the landscape‐of‐fear model: (1) for a single species, we suggest that as the makeup of the landscape of fear changes from relatively safe to relatively risky, bottom‐up impacts switch from strong to weak as top‐down impacts go from weak to strong; (2) for two or more species, interspecific competitive interactions produce various combinations of bottom‐up, top‐down, and parallel impacts depending on the dominant competing species and whether the landscapes of fear are shared or distinctive among competing species. We contend that these predictions could successfully explain many of the complex and contradictory results of current research. We test some of these predictions based on long‐term data for small mammals from the Chihuahuan Desert in the United States. and Mexico. We conclude that the landscape‐of‐fear model does provide reasonable explanations for many of the reported studies and should be tested further to better understand the effects of bottom‐up, top‐down, and parallel factors on population dynamics.

Woody plant abundance is widely recognized to have increased in savannas and grasslands worldwide. The lack of information on the rates, dynamics, and extent of increases in shrub abundance is a major source of uncertainty in assessing how this vegetation change has influenced biogeochemical cycles. Projecting future consequences of woody cover change on ecosystem function will require knowledge of where shrub cover in present‐day stands lies relative to the realizable maximum for a given soil type within a bioclimatic region. We used time‐series aerial photography (1936, 1966, and 1996) and field studies to quantify cover and biomass of velvet mesquite (Prosopis velutina Woot.) following its proliferation in a semidesert grassland of Arizona. Mapping of individual shrubs indicated an encroachment phase characterized by high rates of bare patch colonization. Upon entering a stabilization phase, shrub cover increases associated with recruitment and canopy expansion were largely offset by contractions in canopy area of other shrub patches. Instances of shrub disappearance coincided with a period of below‐average rainfall (1936–1966). Overall, shrub cover (mean ± SE) on sandy uplands with few and widely scattered shrubs in 1902 was dynamically stable over the 1936–1996 period averaging ∼35% ± 5%. Shrub cover on clayey uplands in 1936 was 17% ± 2% but subsequently increased twofold to levels comparable to those on sandy uplands by 1966 (36% ± 7%). Cover on both soils then decreased slightly between 1966 and 1996 to 28% ± 3%. Thus, soil properties influenced the rate at which landscapes reached a dynamic equilibrium, but not the apparent endpoint. Although sandy and clayey landscapes appear to have stabilized at comparable levels of cover, shrub biomass was 1.4 times greater on clayey soils. Declines in shrub cover between 1966 and 1996 were accompanied by a shift to smaller patch sizes on both sandy and clayey landscapes. Dynamics observed during the stabilization phase suggest that density‐dependent regulation may be in play. If woody cover has transitioned from directional increases to a dynamic equilibrium, biomass projections will require monitoring and modeling patch dynamics and stand structure rather than simply changes in total cover.

Near surface (i.e., camera) and satellite remote sensing metrics have become widely used indicators of plant growing seasons. While robust linkages have been established between field metrics and ecosystem exchange in many land cover types, assessment of how well remotely-derived season start and end dates depict field conditions in arid ecosystems remain unknown. We evaluated the correspondence between field measures of start (SOS; leaves unfolded and canopy greenness >0) and end of season (EOS) and canopy greenness for two widespread species in southwestern U.S. ecosystems with those metrics estimated from near-surface cameras and MODIS NDVI for five years (2012–2016). Using Timesat software to estimate SOS and EOS from the phenocam green chromatic coordinate (GCC) greenness index resulted in good agreement with ground observations for honey mesquite but not black grama. Despite differences in the detectability of SOS and EOS for the two species, GCC was significantly correlated with field estimates of canopy greenness for both species throughout the growing season. MODIS NDVI for this arid grassland site was driven by the black grama signal although a mesquite signal was discernable in average rainfall years. Our findings suggest phenocams could help meet myriad needs in natural resource management.