Explore the vast range of titles available.

The Landsat satellites provide decades of near‐global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi‐decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross‐sensor calibration and challenges with irregular timing of cloud‐free acquisitions. To help address these problems, we developed the ‘LandsatTS' package for R. This software package facilitates sample‐based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross‐sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how ‘LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents.

Changes in Arctic vegetation can have important implications for trophic interactions and ecosystem functioning leading to climate feedbacks. Plot-based vegetation surveys provide detailed insight into vegetation changes at sites around the Arctic and improve our ability to predict the impacts of environmental change on tundra ecosystems. Here, we review studies of changes in plant community composition and phenology from both long-term monitoring and warming experiments in Arctic environments. We find that Arctic plant communities and species are generally sensitive to warming, but trends over a period of time are heterogeneous and complex and do not always mirror expectations based on responses to experimental manipulations. Our findings highlight the need for more geographically widespread, integrated, and comprehensive monitoring efforts that can better resolve the interacting effects of warming and other local and regional ecological factors.

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change.As tundra ecosystems respond to rapid Arctic warming, satellite records suggest a widespread greening. This Perspective highlights the challenges of interpreting complex Arctic greening trends and provides direction for future research by combining ecological and remote sensing approaches.

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

The influence of dispersal limitation on species ranges remains controversial. Considering the dramatic impacts of the last glaciation in Europe, species might not have tracked climate changes through time and, as a consequence, their present-day ranges might be in disequilibrium with current climate. For 1016 European plant species, we assessed the relative importance of current climate and limited postglacial migration in determining species ranges using regression modelling and explanatory variables representing climate, and a novel species-specific hind-casting-based measure of accessibility to postglacial colonization. Climate was important for all species, while postglacial colonization also constrained the ranges of more than 50 per cent of the species. On average, climate explained five times more variation in species ranges than accessibility, but accessibility was the strongest determinant for one-sixth of the species. Accessibility was particularly important for species with limited long-distance dispersal ability, with southern glacial ranges, seed plants compared with ferns, and small-range species in southern Europe. In addition, accessibility explained one-third of the variation in species' disequilibrium with climate as measured by the realized/potential range size ratio computed with niche modelling. In conclusion, we show that although climate is the dominant broad-scale determinant of European plant species ranges, constrained dispersal plays an important supplementary role.

The combined effects of climate change and nutrient availability on Arctic vegetation growth are poorly understood. Archaeological sites in the Arctic could represent unique nutrient hotspots for studying the long-term effect of nutrient enrichment. In this study, we analysed a time-series of ring widths of Salix glauca L. collected at nine archaeological sites and in their natural surroundings along a climate gradient in the Nuuk fjord region, Southwest Greenland, stretching from the edge of the Greenlandic Ice Sheet in the east to the open sea in the west. We assessed the temperature-growth relationship for the last four decades distinguishing between soils with past anthropogenic nutrient enrichment (PANE) and without (controls). Along the East–West gradient, the inner fjord sites showed a stronger temperature signal compared to the outermost ones. Individuals growing in PANE soils had wider ring widths than individuals growing in the control soils and a stronger climate-growth relation, especially in the inner fjord sites. Thereby, the individuals growing on the archaeological sites seem to have benefited more from the climate warming in recent decades. Our results suggest that higher nutrient availability due to past human activities plays a role in Arctic vegetation growth and should be considered when assessing both the future impact of plants on archaeological sites and the general greening in landscapes with contrasting nutrient availability.

A common approach for analysing geographical variation in biodiversity involves using linear models to determine the rate at which species similarity declines with geographical or environmental distance and comparing this rate among regions, taxa or communities. Implicit in this approach are weakly justified assumptions that the rate of species turnover remains constant along gradients and that this rate can therefore serve as a means to compare ecological systems. We use generalized dissimilarity modelling, a novel method that accommodates variation in rates of species turnover along gradients and between different gradients, to compare environmental and spatial controls on the floras of two regions with contrasting evolutionary and climatic histories: southwest Australia and northern Europe. We find stronger signals of climate history in the northern European flora and demonstrate that variation in rates of species turnover is persistent across regions, taxa and different gradients. Such variation may represent an important but often overlooked component of biodiversity that complicates comparisons of distance–decay relationships and underscores the importance of using methods that accommodate the curvilinear relationships expected when modelling beta diversity. Determining how rates of species turnover vary along and between gradients is relevant to understanding the sensitivity of ecological systems to environmental change.

Aims Phylogenetic endemism describes the extent to which unique phylogenetic lineages are constrained to restricted geographic areas. Previous studies indicate that species endemism is related to both past and modern climate, but studies of phylogenetic endemism are relatively rare and mainly focused on smaller regions. Here, we provide the first assessment of the patterns of species and phylogenetic endemism in angiosperm trees across the Northern Hemisphere as well as the relative importance of modern climate and glacial–interglacial climate change as drivers of these patterns. Location Northern Hemisphere. Major taxa Angiosperm trees. Methods Using tree assemblages at the scale of 100 km × 100 km grid cells and simultaneous autoregressive (SAR) models, we assessed the relationships between species endemism, phylogenetic endemism and modern climate variables, Last Glacial Maximum (LGM) to present temperature velocity. Results Species and phylogenetic endemism were associated with both modern climate and glacial–interglacial climate change, with higher values in areas with stable historical climate and warmer and wetter modern conditions. Notably, the multivariate SAR analyses showed that the combinations of variables with highest Akaike’s information criterion (AIC) weight always included both LGM–present climate instability and modern climate, that is, modern precipitation and temperature. Main conclusions Our results show that high phylogenetic endemism is partially dependent on long‐term climate stability, highlighting the threat posed by future climate changes to the preservation of rare, phylogenetically distinct lineages of trees.

The effects of climate change on Arctic ecosystems can range between various spatiotemporal scales and may include shifts in population distribution, community composition, plant phenology, primary productivity and species biodiversity. The growth rates and age structure of tundra vegetation as well as its response to temperature variation, however, remain poorly understood because high‐resolution data are limited in space and time. Anatomical and morphological stem characteristics were recorded to assess the growth behaviour and age structure of 871 dwarf shrubs from 10 species at 30 sites in coastal East Greenland at ˜70°N. Recruitment pulses were linked with changes in mean annual and summer temperature back to the 19th century, and a literature review was conducted to place our findings in a pan‐Arctic context. Low cambial activity translates into estimated average/maximum plant ages of 59/204 years, suggesting relatively small turnover rates and stable community composition. Decade‐long changes in the recruitment intensity were found to lag temperature variability by 2 and 6 years during warmer and colder periods, respectively (r = 0.85₁₉₆₁–₂₀₀₀ ₐₙd ₁₈₈₁–₁₉₂₀). Synthesis. Our results reveal a strong temperature dependency of Arctic dwarf shrub reproduction, a high vulnerability of circumpolar tundra ecosystems to climatic changes, and the ability of evaluating historical vegetation dynamics well beyond the northern treeline. The combined wood anatomical and plant ecological approach, considering insights from micro‐sections to community assemblages, indicates that model predictions of rapid tundra expansion (i.e. shrub growth) following intense warming might underestimate plant longevity and persistence but overestimate the sensitivity and reaction time of Arctic vegetation.

Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of Cytisus scoparius homogenizes the landscape negatively affecting local plant diversity. Detecting structural change (e.g., biomass) is essential for assessing negative impacts of encroachment. Hence, exploring new monitoring tools to achieve this task is important for effectively capturing change and evaluating management activities. This study combines traditional field‐based measurements with novel Light Detection and Ranging (LiDAR) observations from an Unmanned Aircraft System (UAS). We investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh‐density point cloud data (>1,000 pts/m2). Presence–absence of 12 shrub or tree genera was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore, 10 individuals of C. scoparius were harvested for biomass measurements. With a UAS LiDAR system, we collected ultrahigh‐density spatial data across the area in October 2017 (leaf‐on) and April 2018 (leaf‐off). We utilized a 3D point‐based classification to distinguish shrub genera based on their structural appearance (i.e., density, light penetration, and surface roughness). From the identified C. scoparius individuals, we related different volume metrics (mean, max, and range) to measured biomass and quantified spatial variation in biomass change from 2017 to 2018. We obtained overall classification accuracies above 86% from point clouds of both seasons. Maximum volume explained 77.4% of the variation in biomass. The spatial patterns revealed landscape‐scale variation in biomass change between autumn 2017 and spring 2018, with a notable decrease in some areas. Further studies are needed to disentangle the causes of the observed decrease, for example, recent winter grazing and/or frost events. Synthesis and applications: We present a workflow for processing ultrahigh‐density spatial data obtained from a UAS LiDAR system to detect change in C. scoparius. We demonstrate that UAS LiDAR is a promising tool to map and monitor grassland shrub dynamics at the landscape scale with the accuracy needed for effective nature management. It is a new tool for standardized and nonbiased evaluation of management activities initiated to prevent shrub encroachment. We developed a workflow for detecting shrub encroachment by Cytisus scoparius in a seminatural Danish grassland. We use highly detailed spatial information from UAV LiDAR point clouds to recognize shrub genera and estimate biomass between two years of monitoring.