Many U.S. national parks are already at the extreme warm end of their historical temperature distributions. With rapidly warming conditions, park resource management will be enhanced by information on seasonality of climate that supports adjustments in the timing of activities such as treating invasive species, operating visitor facilities, and scheduling climate‐related events (e.g., flower festivals and fall leaf‐viewing). Seasonal changes in vegetation, such as pollen, seed, and fruit production, are important drivers of ecological processes in parks, and phenology has thus been identified as a key indicator for park monitoring. Phenology is also one of the most proximate biological responses to climate change. Here, we use estimates of start of spring based on climatically modeled dates of first leaf and first bloom derived from indicator plant species to evaluate the recent timing of spring onset (past 10–30 yr) in each U.S. natural resource park relative to its historical range of variability across the past 112 yr (1901–2012). Of the 276 high latitude to subtropical parks examined, spring is advancing in approximately three‐quarters of parks (76%), and 53% of parks are experiencing “extreme” early springs that exceed 95% of historical conditions. Our results demonstrate how changes in climate seasonality are important for understanding ecological responses to climate change, and further how spatial variability in effects of climate change necessitates different approaches to management. We discuss how our results inform climate change adaptation challenges and opportunities facing parks, with implications for other protected areas, by exploring consequences for resource management and planning.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m²) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n= 122 NAIP state x year combinations; median overall accuracy 0.89–0.90; median Kappa 0.85–0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.

We evaluate the world terrestrial network of protected areas (PAs) for its partnership potential in responding to climate change. That is, if a PA engaged in collaborative, trans-boundary management of species, by investing in conservation partnerships with neighboring areas, what climate change adaptation benefits might accrue? We consider core tenets of conservation biology related to protecting large areas with high environmental heterogeneity and low climate change velocity and ask how a series of biodiversity adaptation indicators change across spatial scales encompassing potential PA and non-PA partners. Less than 1% of current world terrestrial PAs equal or exceed the size of established and successful conservation partnerships. Partnering at this scale would increase the biodiversity adaptation indicators by factors up to two orders of magnitude, compared to a null model in which each PA is isolated. Most partnership area surrounding PAs is comprised of non-PAs (70%), indicating the importance of looking beyond the current network of PAs when promoting climate change adaptation. Given monumental challenges with PA-based species conservation in the face of climate change, partnerships provide a logical and achievable strategy for helping areas adapt. Our findings identify where strategic partnering efforts in highly vulnerable areas of the world may prove critical in safeguarding biodiversity.

The Eastern Arc Mountains of Tanzania and Kenya, a montane archipelago of 13 uplifted fault blocks (sky islands) isolated by lowland arid savanna, are a center of exceptional biological endemism. Under the influence of humid winds from the Indian Ocean, forests and associated species may have persisted in this region since the final uplift of these blocks in the late Miocene. Today, these mountains are inhabited by a remarkable diversity of bird species. To better understand the evolutionary processes behind this diversity, we combined molecular phylogenetic studies of East African montane birds with paleoclimate modeling of its montane forests. Across its largest lowland barrier, the 125 km between the Usambara and Nguru/Nguu Mountains, 10 of the 14 bird lineages exhibited a phylogeographic break. Using Bayesian methods, we established that at least three periods of forest contraction and expansion affected the diversification of Eastern Arc birds. Habitat distribution models suggest that lower-elevation hills may have acted as stepping-stones connecting isolated highlands to allow for the dispersal of montane forest-dependent species across them. Periods of vicariance during paleoclimatic cycles extending back through the Last Glacial Maximum would have then isolated these populations within the highlands they had reached. The broad distribution of neoendemic species across the mountains of East Africa provides evidence of climate cycling as a driver of lineage diversification. The high incidence of narrow-range endemism of paleoendemic species on the Usambara, Uluguru, and Udzungwa Mountains of this region is harder to explain. Our paleoclimate models retrodicted the persistence of montane forest during climate cycles on several Eastern Arc sky islands but not on the Southern Tanzania Volcanic Highlands. Consistent with recent theoretical work, different rates of local extinction rather than increased rates of lineage diversification may explain the pattern of excessive narrow-range endemism on some sky islands over others. Thus, a regional filtering effect is generated, with paleoendemics maintaining populations through time only in areas where habitat persisted, providing a credible explanation for the dramatic variance in levels of endemism among different East African sky islands.

In this chapter, we examine how climate change will likely affect areas of the Great Northern Landscape Conservation Cooperative (Great Northern LCC), but rather than using a fine-filter approach...

Effective management of forest insects and diseases requires detection of abnormal mortality, particularly among a single species, sufficiently early to enable effective management. Remote detection of individual trees crowns requires a spatial resolution not available from satellites such as Landsat or Sentinel-2. In the United States, there are currently few operational systems capable of effectively and affordably detecting and mapping tree mortality over broad landscapes using high-resolution imagery. Here, we introduce the Tree Condition and Analysis Program (TreeCAP), an open-source system that uses freely available imagery from the National Agriculture Imagery Program (NAIP) to create maps of tree condition (healthy or damaged). We demonstrate the potential applications of TreeCAP in four study sites: (1) beetle-killed pines in California, (2) emerald ash borer progression in Wisconsin, (3) hemlock wooly adelgid mortality in Pennsylvania, and (4) drought damage in Texas. We achieved an average overall accuracy of 87% across all study sites.