• Energy Research

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079-189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3-85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253-10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

SummaryUrbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited.Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering).Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions.Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales.

Phenology, or the timing of life history events, can be heterogeneous across biological communities and landscapes and can vary across a wide variety of spatiotemporal scales. Here, we synthesize information from landscape phenology studies across different scales of measurement around a set of core concepts. We highlight why phenology is scale dependent and identify gaps in the spatiotemporal scales of phenological observations and inferences. We discuss the consequences of these gaps and describe opportunities to address the inherent sensitivities of phenological metrics to measurement scale. Although most studies we review and discuss are focused on plants, our work provides a broadly relevant overview of the role of observation scale in landscape phenology and a general approach for measuring and reporting scale dependence.

Earth's most speciose biomes are in the tropics, yet tropical plant phenology remains poorly understood. Tropical phenological data are comparatively scarce and viewed through the lens of a 'temperate phenological paradigm' expecting phenological traits to respond to strong, predictably annual shifts in climate (e.g., between subfreezing and frost-free periods). Digitized herbarium data greatly expand existing phenological data for tropical plants; and circular data, statistics, and models are more appropriate for analyzing tropical (and temperate) phenological datasets. Phylogenetic information, which remains seldom applied in phenological investigations, provides new insights into phenological responses of large groups of related species to climate. Consistent combined use of herbarium data, circular statistical distributions, and robust phylogenies will rapidly advance our understanding of tropical - and temperate - phenology.

A large fraction of Earth’s plant species are faced with increased chances of extinction.

Replication code and data for the paper: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity." The following files are included in this repository: 1) R scripts (numbered 0 through 9) include replication code for data analysis 2) Datasets (6 zip folders) contain the data analyzed in the R scripts

Summary Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon‐specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro‐environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.