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Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive., Data collection was partially supported by the European Union’s Horizon 2020 research and innovation program through grants to the POnTE (Pest Organisms threatening Europe; grant 635646 from European Union’s Horizon 2020 Framework Research Programme) and XF-ACTORS (Xylella fastidiosa Active Containment Through a Multidisciplinary-Oriented Research Strategy; grant 727987 from European Union’s Horizon 2020 Framework Research Programme) projects. A. Hornero was supported by a research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK).

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world’s most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic–abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide., The study was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from ‘Programa Estatal de I + D + I Orientada a los Retos de la Sociedad’ of Spain and FEDER, Intramural Project 201840E111 from CSIC, and Project ITS2017-095 Consejeria de Medio Ambiente, Agricultura y Pesca de las Islas Baleares, Spain. The views expressed are purely those of the writers and may not in any circumstance be regarded as stating an official position of the European Commission.

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution., Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain).

Forest disturbance regimes are expected to intensify as Earth’s climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979–2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes., Natural disturbances imperil healthy and productive forests, but quantifying their effects at large scales is challenging. Here the authors apply machine learning to disturbance records and satellite data to quantify and map European forest vulnerability to fires, windthrows, and insect outbreaks through 1979-2018.

Highlights • We searched for properties making a network intrinsically vulnerable to epidemics. • We conducted simulations on both modelled and real-world contact networks. • Network properties may affect outbreak magnitude more than pathogen features. • We show how structural properties can be used to infer relative network vulnerability., Contact networks are convenient models to investigate epidemics, with nodes and links representing potential hosts and infection pathways, respectively. The outcomes of outbreak simulations on networks are driven both by the underlying epidemic model, and by the networks’ structural properties, so that the same pathogen can generate different epidemic dynamics on different networks. Here we ask whether there are general properties that make a contact network intrinsically vulnerable to epidemics (that is, regardless of specific epidemiological parameters). By conducting simulations on a large set of modelled networks, we show that, when a broad range of network topologies is taken into account, the effect of specific network properties on outbreak magnitude is stronger than that of fundamental pathogen features such as transmission rate, infection duration, and immunization ability. Then, by focusing on a large set of real world networks of the same type (potential contacts between field voles, Microtus agrestis), we showed how network structure can be used to accurately assess the relative, intrinsic vulnerability of networks towards a specific pathogen, even when those have limited topological variability. These results have profound implications for how we prevent disease outbreaks; in many real world situations, the topology of host contact networks can be described and used to infer intrinsic vulnerability. Such an approach can increase preparedness and inform preventive measures against emerging diseases for which limited epidemiological information is available, enabling the identification of priority targets before an epidemic event.

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security1, ecosystem integrity and societies in general2–5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment6. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa’s geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage7,8. X. fastidiosa can infect more than 350 plant species worldwide9, and early detection is critical for its eradication8. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.

The operational monitoring of forest decline requires the development of remote sensing methods that are sensitive to the spatiotemporal variations of pigment degradation and canopy defoliation. In this context, the red-edge spectral region (RESR) was proposed in the past due to its combined sensitivity to chlorophyll content and leaf area variation. In this study, the temporal dimension of the RESR was evaluated as a function of forest decline using a radiative transfer method with the PROSPECT and 3D FLIGHT models. These models were used to generate synthetic pine stands simulating decline and recovery processes over time and explore the temporal rate of change of the red-edge chlorophyll index (CI) as compared to the trajectories obtained for the structure-related Normalized Difference Vegetation Index (NDVI). The temporal trend method proposed here consisted of using synthetic spectra to calculate the theoretical boundaries of the subspace for healthy and declining pine trees in the temporal domain, defined by CItime=n/CItime=n+1 vs. NDVItime=n/NDVItime=n+1. Within these boundaries, trees undergoing decline and recovery processes showed different trajectories through this subspace. The method was then validated using three high-resolution airborne hyperspectral images acquired at 40 cm resolution and 260 spectral bands of 6.5 nm full-width half-maximum (FWHM) over a forest with widespread tree decline, along with field-based monitoring of chlorosis and defoliation (i.e., ‘decline’ status) in 663 trees between the years 2015 and 2016. The temporal rate of change of chlorophyll vs. structural indices, based on reflectance spectra extracted from the hyperspectral images, was different for trees undergoing decline, and aligned towards the decline baseline established using the radiative transfer models. By contrast, healthy trees over time aligned towards the theoretically obtained healthy baseline. The applicability of this temporal trend method to the red-edge bands of the MultiSpectral Imager (MSI) instrument on board Sentinel-2a for operational forest status monitoring was also explored by comparing the temporal rate of change of the Sentinel-2-derived CI over areas with declining and healthy trees. Results demonstrated that the Sentinel-2a red-edge region was sensitive to the temporal dimension of forest condition, as the relationships obtained for pixels in healthy condition deviated from those of pixels undergoing decline.

The majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. Plant trait relationships are thus generalizable to the edge of global trait-space, informing prediction of plant community change in a warming world., It is unclear whether plant trait relationships found at the global scale extend to climatic extremes. Here the authors analyse six major aboveground traits to show that known plant trait relationships extend to the tundra biomes and exhibit the same two dimensions of variation detected at the global scale.

Accurate delimitation of species and their relationships is a fundamental issue in evolutionary biology and taxonomy and provides essential implications for conservation management. Scleractinian corals are difficult to identify because of their ecophenotypic and geographic variation and their morphological plasticity. Furthermore, phylogenies based on traditional loci are often unresolved at the species level because of uninformative loci. Here, we attempted to resolve these issues and proposed a consistent species definition method for corals by applying the genome-wide technique Restriction-site Associated DNA sequencing (RADseq) to investigate phylogenetic relationships and species delimitation within the genus Leptastrea. We collected 77 colonies from nine localities of the Indo-Pacific and subjected them to genomic analyses. Based on de novo clustering, we obtained 44,162 SNPs (3701 loci) from the holobiont dataset and 62,728 SNPs (9573 loci) from the reads that map to coral transcriptome to reconstruct a robust phylogenetic hypothesis of the genus. Moreover, nearly complete mitochondrial genomes and ribosomal DNA arrays were retrieved by reference mapping. We combined concatenation-based phylogenetic analyses with coalescent-based species tree and species delimitation methods. Phylogenies suggest the presence of six distinct species, three corresponding to known taxa, namely Leptastrea bottae, Leptastrea inaequalis, Leptastrea transversa, one characterized by a remarkable skeletal variability encompassing the typical morphologies of Leptastrea purpurea and Leptastrea pruinosa, and two distinct and currently undescribed species. Therefore, based on the combination of genomic, morphological, morphometric, and distributional data, we herein described Leptastrea gibbosa sp. n. from the Pacific Ocean and Leptastrea magaloni sp. n. from the southwestern Indian Ocean and formally considered L. pruinosa as a junior synonym of L. purpurea. Notably, mitogenomes and rDNA yielded a concordant yet less resolved phylogeny reconstruction compared to the ones based on SNPs. This aspect demonstrates the strength and utility of RADseq technology for disentangling species boundaries in closely related species and in a challenging group such as scleractinian corals.

Strong winds may uproot and break trees and represent one of the major natural disturbances for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the climate change effects. Despite the importance of such natural disturbances, there are currently no spatially-explicit databases of wind-related impact at Pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND comprises more than 80,000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018, and describes them in a harmonized and consistent geographical vector format. Correlation analyses performed between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p-valuehttps://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).

Highlights • Pathogens’ long distance dispersal might drive epidemics in agricultural landscapes. • We explored the relevance of long distance dispersal using network analysis. • We simulated insect-born outbreaks of Xylella fastidiosa on olives in Andalusia. • Probability of pathogen’s long distance dispersal dramatically affected outbreaks. • Limiting pathogen’s dispersal might be more effective than local control strategies., Outbreaks of a plant disease in a landscape can be meaningfully modelled using networks with nodes representing individual crop-fields, and edges representing potential infection pathways between them. Their spatial structure, which resembles that of a regular lattice, makes such networks fairly robust against epidemics. Yet, it is well-known how the addition of a few shortcuts can turn robust regular lattices into vulnerable ‘small world’ networks. Although the relevance of this phenomenon has been shown theoretically for networks with nodes corresponding to individual host plants, its real-world implications at a larger scale (i.e. in networks with nodes representing crop fields or other plantations) remain elusive. Focusing on realistic spatial networks connecting olive orchards in Andalusia (Southern Spain), the world’s leading olive producer, we show how even very small probabilities of long distance dispersal of infectious vectors result in a small-world effect that dramatically exacerbates a hypothetical outbreak of a disease targeting olive trees (loosely modelled on known epidemiological information on the bacterium Xylella fastidiosa, an important emerging threat for European agriculture). More specifically, we found that the probability of long distance vector dispersal has a disproportionately larger effect on epidemic dynamics compared to pathogen’s intrinsic infectivity, increasing total infected area by up to one order of magnitude (in the absence of quarantine). Furthermore, even a very small probability of long distance dispersal increased the effort needed to halt a hypothetical outbreak through quarantine by about 50% in respect to scenarios modelling local/short distance pathogen’s dispersal only. This highlights how identifying (and disrupting) long distance dispersal processes may be more efficacious to contain a plant disease epidemic than surveillance and intervention concentrated on local scale transmission processes.

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. acceptedVersion

WOS:000669987000010; Reef fishes are a treasured part of marine biodiversity, and also provide needed protein for many millions of people. Although most reef fishes might survive projected increases in ocean temperatures, corals are less tolerant. A few fish species strictly depend on corals for food and shelter, suggesting that coral extinctions could lead to some secondary fish extinctions. However, secondary extinctions could extend far beyond those few coral-dependent species. Furthermore, it is yet unknown how such fish declines might vary around the world. Current coral mass mortalities led us to ask how fish communities would respond to coral loss within and across oceans. We mapped 6964 coral-reef-fish species and 119 coral genera, and then regressed reef-fish species richness against coral generic richness at the 1 degrees scale (after controlling for biogeographic factors that drive species diversification). Consistent with small-scale studies, statistical extrapolations suggested that local fish richness across the globe would be around half its current value in a hypothetical world without coral, leading to more areas with low or intermediate fish species richness and fewer fish diversity hotspots.

With the advent of Sentinel-2, it is now possible to generate large-scale chlorophyll content maps with unprecedented spatial and temporal resolution, suitable for monitoring ecological processes such as vegetative stress and/or decline. However methodological gaps exist for adapting this technology to heterogeneous natural vegetation and for transferring it among vegetation species or plan functional types. In this study, we investigated the use of Sentinel-2A imagery for estimating needle chlorophyll (Ca+b) in a sparse pine forest undergoing significant needle loss and tree mortality. Sentinel-2A scenes were acquired under two extreme viewing geometries (June vs. December 2016) coincident with the acquisition of high-spatial resolution hyperspectral imagery, and field measurements of needle chlorophyll content and crown leaf area index. Using the high-resolution hyperspectral scenes acquired over 61 validation sites we found the CI chlorophyll index R750/R710 and Macc index (which uses spectral bands centered at 680 nm, 710 nm and 780 nm) had the strongest relationship with needle chlorophyll content from individual tree crowns (r2 = 0.61 and r2 = 0.59, respectively; p 0.7 for June and >0.4 for December; p

This paper attempts to provide an understanding of the potential application of Sentinel-2 imagery for the monitoring and detection of disease symptoms caused by Xylella fastidiosa (Xf) in olive trees. A time series data of 188 Sentinel-2a images collected over the last two years was used to analyse the temporal trends in areas with Xf infected olive trees in Puglia, Southern Italy. The robustness of different physiological and structural hyperspectral indices was evaluated as an early indicator of Xf symptoms. Three validation sites for Sentinel-2a products were hence established over olive orchards in the Xf-infected zone in two different years (2016 and 2017) and overflown with a hyperspectral sensor to acquire high spatial resolution images (50 cm). Disease incidence and severity levels were recorded for more than 3300 olive trees in 18 orchards. Results demonstrate the capability of temporal Sentinel-2a was able to detect and discriminate between high and medium Xf incidence, reaching the maximum differences during the summer season. Among all the vegetation indices evaluated from Sentinel-2 imagery, OSAVI showed superior performance for detecting Xf incidence trends and OSAVI1510 for detecting changes in Xf severity levels.

© 2014 Elsevier Ltd. Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.

Altres ajuts europeus: P.A.W. was additionally supported by the European Union Fourth Environment and Climate Framework Programme (Project Number ENV4-CT970586)P.A.W. was additionally supported by the European Union Fourth Environment and Climate Framework Programme (Project Number ENV4-CT970586). The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.

Olive trees have been of economic and cultural value since pre-Roman times, and continue to dominate landscapes and agriculture in many mediterranean regions. Recent mass losses of olive trees in Southern Italy due to an exotic plant pathogen highlight the need for methods that to monitor the olive trees in a landscape or region operationally. Here, we develop a method for counting olive trees from aerial photographs and test it in areas with a high diversity of olive tree ages, sizes, and shapes. This heterogeneity complicates tree counting as centennial trees often have crowns that are split into multiple segments, resembling multiple crowns, while nearby crowns often form a semi-closed canopy comprising multiple trees. Comparisons with reference counts in two 20 ha sites and over three different years indicate the automated counts tend to be reasonably accurate (median error 13%, n = 6), but heavily influenced by a few olive orchards with particularly high planting densities and a relatively closed canopy in which distinguishing individual trees is challenging. Overall, the algorithm estimated tree densities well (counting 82 to 109 trees/ha versus 87 to 104 trees/ha in the reference counts), indicating the method is suitable to track the number of olive trees over large areas.

The insect vector borne bacterium Xylella fastidiosa was first detected in olive trees in Southern Italy in 2013, and identified as the main culprit behind the ‘olive quick decline syndrome’. Since then, the disease has spread rapidly through Italy’s main olive oil producing region. The epidemiology of the outbreak is largely unstudied, with the list of X. fastidiosa hosts and vectors in Europe likely incomplete, and the role humans play in dispersal unknown. These knowledge gaps have led to management strategies based on general assumptions that require, among others, local vector control and, in certain areas, the destruction of infected plants and healthy ones around them in an attempt to eradicate or halt the spreading pest. Here we show that, regardless of epidemiological uncertainties, the mere distribution of olive orchards in Southern Italy makes the chances of eradicating X. fastidiosa from the region extremely slim. Our results imply that Southern Italy is becoming a reservoir for X. fastidiosa. As a consequence, management strategies should keep the prevalence of X. fastidiosa in the region as low as possible, primarily through vector control, lest the pathogen, that has also been detected in southern France and the island of Mallorca (Spain), continues spreading through Italy and Europe.

[1] Historically, ungulate migrations occurred in most grassland and boreal woodland ecosystems, but many have been lost due to increasing habitat loss and fragmentation. With the rate of environmental change increasing, identifying and prioritizing migration routes for conservation has taken on a new urgency. Understanding the cues that drive long-distance animal movements is critical to predicting the fate of migrations under different environmental change scenarios and how large migratory herbivores will respond to increasing resource heterogeneity and anthropogenic influences. We used an individual-based modeling approach to investigate the influence of environmental conditions, monitored using satellite data, on departure date and movement speed of migrating zebras in Botswana. Daily zebra movements between dry and rainy season ranges were annotated with coincident observations of precipitation from the Tropical Rainfall Measuring Mission data set and Moderate Resolution Imaging Spectroradiometer-derived normalized difference vegetation index (NDVI). An array of increasingly complex movement models representing alternative hypotheses regarding the environmental cues and controls for movement was parameterized and tested. The best and most justified model predicted daily zebra movement as two linear functions of precipitation rate and NDVI and included a modeled departure date as a function of cumulative precipitation. The model was highly successful at replicating both the timing and pace of seven actual migrations observed using GPS telemetry (R2 = 0.914). It shows how zebras rapidly adjust their movement to changing environmental conditions during migration and are able to reverse migration to avoid adverse conditions or exploit renewed resource availability, a nomadic behavior which should lend them a degree of resilience to climate and environmental change. Our results demonstrate how competing individual-based migration models, informed by freely available satellite data, can be used to evaluate the weight of evidence for multiple hypotheses regarding the use of environmental cues in animal movement. This modeling framework can be applied to quantify how animals adapt the timing and pace of their movements to prevailing environmental conditions and to forecast migrations in near real time or under alternative environmental scenarios.

This study shows that climate change could lead to a major redistribution of vegetation across the Arctic, with important implications for biosphere–atmosphere interactions, as well as for biodiversity conservation and ecosystem services. Woody vegetation is predicted to expand substantially over coming decades, causing more Arctic warming through positive climate feedbacks than previously thought.

We compared tree-ring width (TRW) and maximum latewood density (MXD) chronologies to remotely sensed indices of productivity (NDVI) and snowmelt since 1981 and to the instrumental temperature record at four arctic treeline sites in North America. Our results show that at these sites, TRW chronologies reflect temperatures less consistently than the MXD chronologies do and that the NDVI does not correlate significantly with TRW at high-frequency, i.e. when comparing yearly values. In contrast, the MXD chronologies correlate positively and significantly with NDVI and temperature during the growing season at all sites. Neither TRW or MXD chronologies appeared consistently influenced by the annual timing of snowmelt. A comparison of tree-ring chronologies and temperatures since 1900 confirms that MXD has tracked growing season temperature at these treeline sites throughout the past century. A spatial evaluation of the correlations further reveals that each of the MXD chronologies investigated here reflects interannual variation in NDVI and growing season temperatures across a large geographic region. As a result, they collectively provide a spatially comprehensive record of historic early-season canopy status as well as growing season temperatures for the high latitudes of North America.

Conserving biological diversity given ongoing environmental changes requires the knowledge of how organisms respond biologically to these changes; however, we rarely have this information. This data deficiency can be addressed with coordinated monitoring programs that provide field data across temporal and spatial scales and with process-based models, which provide a method for predicting how species, in particular migrating species that face different conditions across their range, will respond to climate change. We evaluate whether environmental conditions in the wintering grounds of broad-tailed hummingbirds influence physiological and behavioral attributes of their migration. To quantify winter ground conditions, we used operative temperature as a proxy for physiological constraint, and precipitation and the normalized difference vegetation index (NDVI) as surrogates of resource availability. We measured four biological response variables: molt stage, timing of arrival at stopover sites, body mass, and fat. Consistent with our predictions, we found that birds migrating north were in earlier stages of molt and arrived at stopover sites later when NDVI was low. These results indicate that wintering conditions impact the timing and condition of birds as they migrate north. In addition, our results suggest that biologically informed environmental surrogates provide a valuable tool for predicting how climate variability across years influences the animal populations.

Increased fire activity within boreal forests could affect global terrestrial carbon (C) stocks by decreasing stand age or altering tree recruitment, leading to patterns of forest regrowth that differ from those of pre-fire stands. To improve our understanding of post-fire C accumulation patterns within boreal forests, we evaluated above- and belowground C pools within 17 Cajander larch (Larix cajanderi) stands of northeastern Siberia that varied in both years since fire and stand density. Early-successional stands ( 70-year old), aboveground larch biomass, ANPPtree, and soil organic layer C pools increased with stand age. These stands were low density and multi-aged, containing both mature trees and new recruits. The rapid accumulation of aboveground larch biomass in high-density, mid-successional stands allowed them to obtain C stocks similar to those in much older low-density stands (~8,000 g C m−2). If fire frequency increases without altering stand density, landscape-level C storage could decline, but if larch density also increases, large aboveground C pools within high-density stands could compensate for a shorter successional cycle.

Deforestation contributes 6–17% of anthropogenic carbon dioxide emissions. However, much uncertainty in the calculation of deforestation emissions stems from the inadequacy of forest carbon-density and deforestation data. Now an analysis provides the most-detailed estimate so far of the carbon density of vegetation and the associated carbon dioxide emissions from deforestation for ecosystems across the tropics.

In situ observations show increases in shrub cover in different arctic regions in recent decades and have been cited to explain the increases in arctic vegetation productivity revealed by satellite remote sensing. A widespread increase in shrub cover, particularly tall shrub cover, is likely to profoundly alter the tundra biome because of its influence on biogeochemical cycling and feedbacks to climate. To monitor changes in shrub cover, aid field studies, and inform ecosystem models, we mapped shrub cover across the North Slope of Alaska. First, images from the IKONOS and SPOT satellite sensors were used to detect tall (>1 m) and short shrub presence at high resolution (

Resource assessments of small-scale fisheries in the Amazon are rare, making it difficult to improve fisheries management. This study assessed the sustainability of small-scale fishery resources in the Lower Amazon region using data from over 20,000 interviews collected in three floodplain fishing communities between 1992 and 2007. The data were analyzed with respect to theoretical maxima of catches and historical trends of (i) catch per unit effort (CPUE), (ii) species composition of the catch, and (iii) mean body length of the most-caught species. The most important results were: First, observed catches have been about 54–58% of theoretical maxima in all communities. Second, CPUE has remained stable over time in all communities. Third, there has been no substitution of fish species in the catch. Fourth, mean body lengths of five of the nine most-caught species were below length-at-first-reproduction, and the body lengths of some have increased overtime while that of others have remained stable or decreased. Overall, fishery resources in the region appear to be moderately exploited, with some key species showing typical signs of overexploitation. This would indicate that the principal threat to the sustainability of fishery resources is excessive concentration of fishing effort on a few target species combined with unsustainable fishing practices. Management and research recommendations are proposed.

Global vegetation models predict that boreal forests are particularly sensitive to a biome shift during the 21st century. This shift would manifest itself first at the biome!s margins, with evergreen forest expanding into current tundra while being replaced by grasslands or temperate forest at the biome!s southern edge. We evaluated changes in forest productivity since 1982 across boreal Alaska by linking satellite estimates of primary productivity and a large tree-ring data set. Trends in both records show consistent growth increases at the boreal‐tundra ecotones that contrast with drought-induced productivity declines throughout interior Alaska. These patterns support the hypothesized effects of an initiating biome shift. Ultimately, tree dispersal rates, habitat availability and the rate of future climate change, and how it changes disturbance regimes, are expected to determine where the boreal biome will undergo a gradual geographic range shift, and where a more rapid decline.

Drought exerts a strong influence on tropical forest metabolism, carbon stocks, and ultimately the flux of carbon to the atmosphere. Satellite-based studies have suggested that Amazon forests green up during droughts because of increased sunlight, whereas field studies have reported increased tree mortality during severe droughts. In an effort to reconcile these apparently conflicting findings, we conducted an analysis of climate data, field measurements, and improved satellite-based measures of forest photosynthetic activity. Wet-season precipitation and plant-available water (PAW) decreased over the Amazon Basin from 1996−2005, and photosynthetically active radiation (PAR) and air dryness (expressed as vapor pressure deficit, VPD) increased from 2002–2005. Using improved enhanced vegetation index (EVI) measurements (2000–2008), we show that gross primary productivity (expressed as EVI) declined with VPD and PAW in regions of sparse canopy cover across a wide range of environments for each year of the study. In densely forested areas, no climatic variable adequately explained the Basin-wide interannual variability of EVI. Based on a site-specific study, we show that monthly EVI was relatively insensitive to leaf area index (LAI) but correlated positively with leaf flushing and PAR measured in the field. These findings suggest that production of new leaves, even when unaccompanied by associated changes in LAI, could play an important role in Basin-wide interannual EVI variability. Because EVI variability was greatest in regions of lower PAW, we hypothesize that drought could increase EVI by synchronizing leaf flushing via its effects on leaf bud development.

The expansion of invasive alien species is considered a major threat to forest ecosystems and biodiversity. Their potential impacts range from local changes in species composition to wider-scale effects on forest habitat and landscape functioning, although the latter has been relatively little explored in the literature. Here, we assessed the impact of an invasive forest pest, the pine wood nematode (PWN), in the Natura 2000 network of protected areas (PAs) in Portugal, the first European country in which PWN was reported. We considered the impacts of the pest’s spread (up to 2016) on individual PAs, in terms of the fraction of their coniferous forest infected, and on the corridors between PAs, which were mapped and prioritized through least-cost path modelling, geographic information system analysis, and the graph-based probability of connectivity metric. We found that PWN by 2016 had spread into 49% of the Portuguese Natura 2000 coniferous forest habitat, while it had invaded 68% of the coniferous forests that form the priority corridors between the PAs. These impacts are likely to be aggravated in the next years, given the pace of PWN expansion and the predicted rates of natural spread to new areas in Portugal and, increasingly likely, in Spain. Our results suggest that the connectivity of PA systems may be significantly disrupted by alien species, and that spatially prioritized control measures can help mitigate the impacts of invasive species on the coherence and functionality of protected area networks such as Natura 2000.

Giant pandas (Ailitropoda melanoleuca) and golden takin (Budorcas taxicolor bedfordi) are large mammals that occur together throughout the southern part of the Qin ling Mountains in China. Both species have the habit of altitudinal migration in a mixed forest-bamboo landscape. Although previous studies have reported that the migration patterns of giant pandas and golden takin seem different, little is known about these differences in relation to their food quality and quantity. We used radio-telemetry data from six giant pandas and three golden takin groups to determine whether differences in their migration patterns are related to satellite-derived plant phenology (a surrogate of food quality) and bamboo abundance (a surrogate of food quantity). Our results suggest that the altitudinal migration patterns of both the giant panda and the golden takin follow the phenological development of plants in the study area, and the difference between them seems to be attributable to the difference in the phenology of bamboo and non-bamboo plants, and thus the abundance and quality of food available to these two species.

Seasonal asymmetry in the interannual variations in the daytime and nighttime climate in the Northern Hemisphere (NH) is well documented, but its consequences for vegetation activity remain poorly understood. Here, we investigate the interannual responses of vegetation activity to variations of seasonal mean daytime and nighttime climate in NH (>30 °N) during the past decades using remote sensing retrievals, FLUXNET and tree ring data. Despite a generally significant and positive response of vegetation activity to seasonal mean maximum temperature ("Equation missing") in ~22–25% of the boreal (>50 °N) NH between spring and autumn, spring-summer progressive water limitations appear to decouple vegetation activity from the mean summer "Equation missing", particularly in climate zones with dry summers. Drought alleviation during autumn results in vegetation recovery from the marked warming-induced drought limitations observed in spring and summer across 24–26% of the temperate NH. Vegetation activity exhibits a pervasively negative correlation with the autumn mean minimum temperature, which is in contrast to the ambiguous patterns observed in spring and summer. Our findings provide new insights into how seasonal asymmetry in the interannual variations in the mean daytime and nighttime climate interacts with water limitations to produce spatiotemporally variable responses of vegetation growth.

An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, (1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel-specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and (2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km×5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates (root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.

We investigate which bedrock-derived soil nutrients are most important for the floristic composition and diversity of alpine, exposed, rocky habitats in North Norway, and whether the widely used pH parameter is a good estimate of soil chemical conditions in the middle alpine belt. Vegetation at 17 sites was recorded using the grid frequency method. Differences in the diversity of vascular plants, bryophytes, and lichens on the different substrates were compared using Shannon's diversity index. Local soil consisted of weathered material from one of five bedrock categories: carbonate rocks, calcareous mica schist, noncalcareous mica schist, mafic rocks, and felsic rocks. Particle size, loss on ignition, pH, and levels of calcium, magnesium, potassium, phosphorus, and nitrogen were measured in soil samples. Canonical Correspondence Analysis showed that soil pH and bedrock derived phosphorus in the soil influence the floristic composition. Randomization tests further indicated that the floristic vari...

Fennoscandia is characterized by a large degree of climatic diversity. Vegetation phenology may respond differently to climate change according to the climatic gradients within the region. To map the annual and spatial variability of the start of the growing season (SOS) in Fennoscandia, the twice-monthly GIMMS-NDVI satellite dataset was used. The data set has an 8 x 8 km(2) spatial resolution and covers the period from 1982 to 2002. The mapping was done by applying pixel-specific threshold values to the NDVI data. These threshold values were determined form surface phenology data on birch (Betula sp.). Then, we produced NDVI based maps of SOS for each of the 21 years. Finally, the time differences between the SOS and the last day of snow cover, as well as dates of passing different temperatures, were analyzed for 21 meteorological stations. The analyses showed that 1985 was the most extreme year in terms of late SOS. In terms of early SOS, the year 1990 was by far the most extreme. Locally, the SOS has an average range of 1 month between the earliest and latest recorded SOS, with a trend towards a bigger range in the oceanic parts. The results indicate that a 1 degrees C increase in spring temperatures in general corresponds to an advancement of 5-6 days in SOS. However, there is a clear trend according to the degree of oceanity, with a 1 degrees C increase in the most oceanic parts corresponding roughly to 7-9 days earlier SOS, compared to less than 5 days earlier in the continental parts.

Despite the intensive research effort directed at predicting the effects of climate change on plants in the Arctic, the impact of environmental change on species' distributions remains difficult to quantify. Predictive habitat distribution models provide a tool to predict the geographical distribution of a species based on the ecological gradients that determine it, and to estimate how the distribution of a species might respond to environmental change. Here, we present a model of the distribution of the dwarf shrub Dryas octopetala L. around the fjord Kongsfjorden, Svalbard. The model was built from field observations, an Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) image, a GIs database containing environmental data at a spatial resolution of 20 m, and relied on generalized linear models (GLMs). We used a logistic GLM to predict the occurrence of the species and a Gaussian GLM to predict its abundance at the sites where it occurred. Temperature and topographical exposure and inclination of a site appeared to promote both the occurrence and the abundance of D. octopetala. The occurrence of the species was additionally negatively influenced by snow and water cover and topographical exposure towards the north, whereas the abundance of the species appeared lower on calciferous substrates. Validation of the model using independent data and the resulting distribution map showed that they successfully recover the distribution of D. octopetala in the study area (kappa = 0.46, AUC =0.81 for the logistic GLM [n - 200], r(2) = 0.29 for the Gaussian GLM [n - 36]). The results further highlight that models predicting the local distribution of plant species in an Arctic environment would greatly benefit from data on the distribution and duration of snow cover. Furthermore, such data are necessary to make quantitative estimates for the impact of changes in temperature and winter precipitation on the distribution of plants in the Arctic. (C) 2005 Elsevier Inc. All rights reserved.