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Urbanization is a global multidimensional process paired with increasing uncertainty due to climate change, migration of people, and changes in the capacity to sustain ecosystem services. This article lays a foundation for discussing transitions in urban governance, which enable cities to navigate change, build capacity to withstand shocks, and use experimentation and innovation in face of uncertainty. Using the three concrete case cities— New Orleans, Cape Town, and Phoenix—the article analyzes thresholds and cross-scale interactions, and expands the scale at which urban resilience has been discussed by integrating the idea from geography that cities form part of “system of cities” (i.e., they cannot be seen as single entities). Based on this, the article argues that urban governance need to harness social networks of urban innovation to sustain ecosystem services, while nurturing discourses that situate the city as part of regional ecosystems. The article broadens the discussion on urban resilience while challenging resilience theory when addressing human-dominated ecosystems. Practical examples of harnessing urban innovation are presented, paired with an agenda for research and policy.

Many of the challenges conservation professionals face can be framed as scale mismatches. The problem of scale mismatch occurs when the planning for and implementation of conservation actions is at a scale that does not reflect the scale of the conservation problem. The challenges in conservation planning related to scale mismatch include ecosystem or ecological process transcendence of governance boundaries; limited availability of fine-resolution data; lack of operational capacity for implementation; lack of understanding of social-ecological system components; threats to ecological diversity that operate at diverse spatial and temporal scales; mismatch between funding and the long-term nature of ecological processes; rate of action implementation that does not reflect the rate of change of the ecological system; lack of appropriate indicators for monitoring activities; and occurrence of ecological change at scales smaller or larger than the scale of implementation or monitoring. Not recognizing and accounting for these challenges when planning for conservation can result in actions that do not address the multiscale nature of conservation problems and that do not achieve conservation objectives. Social networks link organizations and individuals across space and time and determine the scale of conservation actions; thus, an understanding of the social networks associated with conservation planning will help determine the potential for implementing conservation actions at the required scales. Social-network analyses can be used to explore whether these networks constrain or enable key social processes and how multiple scales of action are linked. Results of network analyses can be used to mitigate scale mismatches in assessing, planning, implementing, and monitoring conservation projects. Muchos de los retos que enfrentan los profesionales de la conservación pueden ser catalogados como discordancia de escalas. El problema de discordancia de escalas ocurre cuando la planificación e implementación de acciones de conservación se llevan a cabo en una escala que no refleja la escala del problema de conservación. Los retos de la planificación de la conservación relacionados con la discordancia de escala incluyen el rebase de límites de gobernanza por los procesos ecológicos; la disponibilidad limitada de datos de resolución fina; la carencia de capacidad operativa para la implementación; la falta de entendimiento de los componentes socio-ecológicos del sistema; amenazas a la diversidad ecológica que operan en escalas espaciales y temporales diversas; discordancia entre el financiamiento y la naturaleza a largo plazo de los procesos ecológicos; tasa de implementación de acciones que no refleja la tasa de cambio del sistema ecológico; ausencia de indicadores apropiados de las actividades de monitoreo y ocurrencia de cambio ecológico en escalas menores o mayores que la escala de implementación o monitoreo. El no reconocimiento y consideración de estos retos al planificar la conservación puede resultar en acciones que no abordan la naturaleza multiescala de los problemas de conservación y que no se alcancen los objetivos de conservación. Las redes sociales enlazan organizaciones e individuos en el tiempo y espacio y determinan la escala de las acciones de conservación; por lo tanto, el entendimiento de las redes sociales asociadas con la planificación de la conservación ayudará a determinar el potencial para la implementación de acciones de conservación en las escalas requeridas. El análisis de redes sociales puede ser utilizado para explorar si esas redes constriñen o facilitan procesos sociales claves y como se relacionan las múltiples escalas de acción. Los resultados del análisis de redes pueden ser utilizados para mitigar la discordancia de escalas en la evaluación, planificación, implementación y monitoreo de proyectos de conservación.

A major objective of ecology is to understand how ecological processes limit population connectivity and species' distributions. By spatially quantifying ecological components driving functional connectivity, we can understand why some locally suitable habitats are unoccupied, resulting in observed discontinuities in distribution. However, estimating connectivity may be difficult due to population stochasticity and violations of assumptions of parametric statistics. To address these issues, we present a novel application of Random Forests to landscape genetic data. We address the effects of three key ecological components on Bufo boreas connectivity in Yellowstone National Park: ecological process, scale, and hierarchical organization. Habitat permeability, topographic morphology, and temperature—moisture regime are all significant ecological processes associated with B. boreas connectivity. Connectivity was influenced by growing-season precipitation, 1988 Yellowstone fires, cover, temperature, impervious surfaces (roads and development), and topographic complexity (56% variation explained). We found that habitat permeability generally operates on fine scales, while topographic morphology and temperature—moisture regime operate across multiple scales, thus demonstrating the importance of cross-scale analysis for ecological interpretation. In a hierarchical analysis, we were able to explain more variation within genetic clusters as identified using Structure (a Bayesian algorithm) (74%; dispersal cover, growing-season precipitation, impervious surfaces) as opposed to between genetic clusters (45%; ridgelines, hot, dry slopes, length of hot season, and annual precipitation). Finally, the analytical methods we developed are powerful and can be applied to any species or system with appropriate landscape and genetic data.

The most widespread response to global warming among alpine treeline ecotones is not an upward shift, but an increase in tree density. However, the impact of increasing density on interactions among trees at treeline is not well understood. Here, we test if treeline densification induced by climatic warming leads to increasing intraspecific competition. We mapped and measured the size and age of Smith fir trees growing in two treelines located in the southeastern Tibetan Plateau. We used spatial point-pattern and codispersion analyses to describe the spatial association and covariation among seedlings, juveniles, and adults grouped in 30-yr age classes from the 1860s to the present. Effects of competition on tree height and regeneration were inferred from bivariate mark-correlations. Since the 1950s, a rapid densification occurred at both sites in response to climatic warming. Competition between adults and juveniles or seedlings at small scales intensified as density increased. Encroachment negatively affected height growth and further reduced recruitment around mature trees. We infer that tree recruitment at the studied treelines was more cold-limited prior to 1950 and shifted to a less temperature-constrained regime in response to climatic warming. Therefore, the ongoing densification and encroachment of alpine treelines could alter the way climate drives their transitions toward subalpine forests.

Experiments in ecology are usually designed to provide tests of hypotheses on the influence of the mean intensity of causal processes, whereas the variance around mean effects has been largely overlooked as a causal force in biological assemblages. Repetition of experiments in space and time provides an estimate of this variability at specific scales, but does not explain how changes in variance generate structure in assemblages and the extent to which variance and mean intensity interact. This paper seeks to identify suitable procedures for empirical analyses on the influence of variance and mean intensity of predictor ecological variables on spatial and temporal patterns in natural populations. A survey of the ecological literature indicates that temporal variability in studies of disturbance and in analyses of consumer-resource interactions is generally expressed in terms of frequency of events. This is inappropriate, as frequency confounds the variance with the mean effect size of a process. A possible solution to the problem involves experimental designs in which levels of intensity and those of variability are chosen independently over explicit spatial or temporal scales and treated as fixed, orthogonal factors. Examples are offered for various scenarios of consumer-resource interactions along with indications for statistical tests of hypotheses. Such novel approaches have important ramifications for understanding variability in a wide range of ecological contexts and for predicting the response of assemblages to increased environmental fluctuations, including those expected under modified climate conditions.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) - aircraft remotely operated from the ground - and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range.

Deterministic theories in community ecology suggest that local, niche-based processes, such as environmental filtering, biotic interactions and interspecific trade-offs largely determine patterns of species diversity and composition. In contrast, more stochastic theories emphasize the importance of chance colonization, random extinction and ecological drift. The schisms between deterministic and stochastic perspectives, which date back to the earliest days of ecology, continue to fuel contemporary debates (e.g. niches versus neutrality). As illustrated by the pioneering studies of Robert H. MacArthur and co-workers, resolution to these debates requires consideration of how the importance of local processes changes across scales. Here, we develop a framework for disentangling the relative importance of deterministic and stochastic processes in generating site-to-site variation in species composition (β-diversity) along ecological gradients (disturbance, productivity and biotic interactions) and among biogeographic regions that differ in the size of the regional species pool. We illustrate how to discern the importance of deterministic processes using nullmodel approaches that explicitly account for local and regional factors that inherently create stochastic turnover. By embracing processes across scales, we can build a more synthetic framework for understanding how niches structure patterns of biodiversity in the face of stochastic processes that emerge from local and biogeographic factors.

We argue for expanding the role of theory in ecology to accelerate scientific progress, enhance the ability to address environmental challenges, foster the development of synthesis and unification, and improve the design of experiments and large-scale environmental-monitoring programs. To achieve these goals, it is essential to foster the development of what we call efficient theories, which have several key attributes. Efficient theories are grounded in first principles, are usually expressed in the language of mathematics, make few assumptions and generate a large number of predictions per free parameter, are approximate, and entail predictions that provide well-understood standards for comparison with empirical data. We contend that the development and successive refinement of efficient theories provide a solid foundation for advancing environmental science in the era of big data.