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Urban forests are critical infrastructure for mitigating environmental and social challenges cities face. Municipalities and non-governmental entities, among others, often set goals (e.g., tree planting or canopy targets) to support urban forests and their benefits. We develop the conceptual underpinnings for an analysis of where additional canopy can fit within the landscape, while considering factors that influence where trees can be planted, and canopy can grow (“practical canopy”). We apply this in New York City (NYC) to inform the setting of a canopy goal by the NYC Urban Forest Task Force (UFTF) for the NYC Urban Forest Agenda, which may trigger a virtuous cycle, or a positive feedback loop where people are mobilized to protect the urban forest, and its benefits that ultimately motivate people to commit to its conservation. We further develop framing for a “priority canopy” analysis to understand where urban forest expansion should be prioritized given more context (e.g., environmental hazards and local preferences), which can inform how expansion of the urban forest is achieved. We estimate an opportunity for 15,899 ha of new canopy in NYC given existing opportunities and constraints (practical canopy), which, if leveraged, could result in nearly doubling the canopy as of 2017 (17,253 ha). However, like existing canopy, practical canopy is not evenly distributed, in general, or across jurisdictions and land uses. Relying solely on areas identified as practical canopy to expand the urban forest would exacerbate these inequities. We discuss how the NYC UFTF established a visionary and achievable goal of at least 30% canopy cover by 2035, informed by this analysis and guided by priorities of equity, health, and resilience. Achievement of this goal will ultimately require a combination of protecting and stewarding the existing resource, and leveraging opportunities for tree planting. Achieving a more equitable urban forest will also require identification of priority canopy, and, in cases, creation of new opportunities for tree planting and canopy expansion. Overall, the collaborative establishment of such goals based on local context can be instrumental in creating a virtuous cycle, moving conservation actors toward exercising influence and agency within the social–ecological system.

Abstract Background Global biodiversity is decreasing at an alarming rate and amphibians are at the forefront of this crisis. Understanding the factors that negatively impact amphibian populations and effectively monitoring their health are fundamental to addressing this epidemic. Plasma glucocorticoids are often used to assess stress in amphibians and other vertebrates, but these hormones can be extremely dynamic and impractical to quantify in small organisms. Transcriptomic responses to stress hormones in amphibians have been largely limited to laboratory models, and there have been few studies on vertebrates that have evaluated the impact of multiple stressors on patterns of gene expression. Here we examined the gene expression patterns in tail tissues of stream-dwelling salamanders (Eurycea tynerensis) chronically exposed to the stress hormone corticosterone under different temperature regimes. Results We found unique transcriptional signatures for chronic corticosterone exposure that were independent of temperature variation. Several of the corticosterone responsive genes are known to be involved in immune system response (LY-6E), oxidative stress (GSTM2 and TRX), and tissue repair (A2M and FX). We also found many genes to be influenced by temperature (CIRBP, HSC71, HSP40, HSP90, HSP70, ZNF593). Furthermore, the expression patterns of some genes (GSTM2, LY-6E, UMOD, ZNF593, CIRBP, HSP90) show interactive effects of temperature and corticosterone exposure, compared to each treatment alone. Through a series of experiments we also showed that stressor induced patterns of expression were largely consistent across ages, life cycle modes, and tissue regeneration. Conclusions Outside of thermal stressors, the application of transcriptomes to monitor the health of non-human vertebrate systems has been vastly underinvestigated. Our study suggests that transcriptomic patterns harbor stressor specific signatures that can be highly informative for monitoring the diverse stressors of amphibian populations.

Green roofs provide multiple benefits including reducing the urban heat island effect, absorbing stormwater and air pollution, and serving as habitat for wildlife. However, many cities have not taken advantage of green roofs as a nature-based solution. In New York City (NYC), approximately 20% of the landscape is covered by buildings, thus rooftops present a substantial opportunity for expanding green infrastructure. Spatial data on green roofs are critical for understanding their abundance and distribution, what filters may drive spatial patterns, and who benefits from them. We describe the development of a green roof dataset for NYC based on publicly available data and classification of aerial imagery from 2016. Of the over one million buildings in NYC, we found only 736 with green roofs (

The metacoupling framework provides grounds for characterizing interactions within and between coupled human and natural systems, yet few studies quantify the nuances of these systems. Network analysis is a powerful and flexible tool that has been used to quantify social, economic, and ecological systems. Our objective was to evaluate the utility of network analysis for quantifying metacoupled systems by assessing global soybean trade among 217 countries from 1986 to 2013. We identified and quantified sending and receiving systems, subnetworks and flow pathways, changes over time and across scales, feedbacks, and associations between trade and tropical deforestation. Although a total of 165 distinct cliques were identified within the network, a few key players were disproportionately influential in the 2872 partnerships, including Brazil (37.5%), China (48.6%), and the USA (72.3%). Total network density increased five-fold over the study period with an increasingly smaller set of countries heavily engaged in trade, posing sustainability and food security concerns. We found evidence of a positive feedback where countries with established trade partnerships were more likely to expand trade relationships over the study period. Trade patterns were not explained by regional or continental geography, highlighting limitations of neighborhood analyses commonly used in ecology. We also found evidence of a link between soybean trade and tropical deforestation; in pantropical countries participating in soybean trade, cumulative soybean exports for the period 2000-2012 were strongly associated with remotely sensed estimates of forest loss by country (Rsq = 0.35 , p < 0.0001). We demonstrated that network analyses can be used to quantitatively assess relationships between metacoupled social-ecological systems. Increased data access and platforms for integrating diverse data sources using multidisciplinary tools will be key to pushing the boundaries of quantitative metacoupled systems research.

Species distribution models are used for numerous purposes such as predicting changes in species' ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species' current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.