Predicting krill swarm characteristics important for marine predators foraging off East Antarctica

Open ocean predator‐prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large‐scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional‐scale spatial predictions using a 10‐yr remotely‐sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid‐summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill‐dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.     

Vegetation dynamics and avian seasonal migration: clues from remotely sensed vegetation indices and ecological niche modelling

Aim Regional movements of tropical birds are among the least understood patterns of migration, generally assumed to be related to seasonality of vegetation and food resources. We used readily available remotely sensed data to analyse this relationship in the three‐wattled bellbird (Procnias tricarunculatus), an IUCN Vulnerable canopy frugivorous bird that undergoes localized altitudinal and latitudinal movements between several non‐breeding and breeding ranges. Location Central America. Methods We generated ecological niche models (GARP and Maxent) based on remotely sensed vegetation indices (enhanced vegetation index, EVI; red index, RI; and normalized difference water index, NDWI) that were used as proxies for canopy characteristics and phenological changes between seasons. The variation in EVI was also analysed in relation to known bellbird seasonal migration patterns. Results Remotely sensed summaries of intra‐annual vegetation variation could not explain known movement patterns of bellbirds breeding in Monteverde Cloud Forest Reserve, Costa Rica. The ecological niche modelling (ENM) framework used for exploring potential movement patterns of another population of bellbirds, observed in March 2004 in Corcovado National Park, Costa Rica, showed that the Atlantic and Pacific lowlands are suitable for the bellbirds year‐round, while middle elevations, where breeding may occur, have limited suitability. Main conclusions Our findings suggest that: (1) the Corcovado population tracks similar environmental conditions during the non‐breeding season; (2) migration to middle elevation breeding sites might be related to factors other than vegetation seasonality or breeding might not be restricted to these elevation ranges; and (3) the lowlands comprising most of the suitable areas for bellbirds are where anthropogenic pressure is highest, so conservation status should consider this threat, and conservation planning should emphasize these areas.     

RNA:DNA hybrids are a novel molecular pattern sensed by TLR9

The sensing of nucleic acids by receptors of the innate immune system is a key component of antimicrobial immunity. RNA:DNA hybrids, as essential intracellular replication intermediates generated during infection, could therefore represent a class of previously uncharacterised pathogen‐associated molecular patterns sensed by pattern recognition receptors. Here we establish that RNA:DNA hybrids containing viral‐derived sequences efficiently induce pro‐inflammatory cytokine and antiviral type I interferon production in dendritic cells. We demonstrate that MyD88‐dependent signalling is essential for this cytokine response and identify TLR9 as a specific sensor of RNA:DNA hybrids. Hybrids therefore represent a novel molecular pattern sensed by the innate immune system and so could play an important role in host response to viruses and the pathogenesis of autoimmune disease.     

Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa

Questions: What are the patterns of remotely sensed vegetation phenology, including their inter‐annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Location: South Africa. Method: Ten‐day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long‐term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Results: The phenology metrics (e.g. start of growing season) showed a clear relationship with the seasonality of rainfall, i.e. winter and summer growing seasons. The distribution of the productivity metrics, LI and SI were significantly correlated with mean annual precipitation. The regression tree initially split the biomes based on vegetation production and then by the seasonality of growth. A regression tree was used to produce a predicted biome map with a high level of accuracy (73%). Main conclusion: Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate‐based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.     

The sensor kinase KdpD of Escherichia coli senses external K+

The Kdp system of Escherichia coli is composed of the high‐affinity K⁺ transporter KdpFABC and the two regulatory proteins KdpD (sensor kinase) and KdpE (response regulator), which constitute a typical two‐component system. The kdpFABC operon is induced under K⁺‐limiting conditions and, to a lesser extent, under high osmolality in the medium. In search for the stimulus sensed by KdpD, we studied the inhibitory effect of extracellular K⁺ on the Kdp system at pH 6.0, which is masked by unspecific K⁺ transport at higher pH values. Based on KdpD derivatives carrying single aspartate replacements in the periplasmic loops which are part of the input domain, we concluded that the inhibition of the Kdp system at extracellular K⁺ concentrations above 5 mM is mediated via KdpD/KdpE and not due to inhibition of the K⁺‐transporting KdpFABC complex. Furthermore, time‐course analyses of kdpFABC expression revealed that a decline in the extracellular K⁺ concentration efficiently stimulates KdpD/KdpE‐mediated signal transduction. In this report we provide evidence that the extracellular K⁺ concentration serves as one of the stimuli sensed by KdpD.     

Comparative landscape genetics of pond‐breeding amphibians in Mediterranean temporal wetlands: The positive role of structural heterogeneity in promoting gene flow

Comparative landscape genetics studies can provide key information to implement cost‐effective conservation measures favouring a broad set of taxa. These studies are scarce, particularly in Mediterranean areas, which include diverse but threatened biological communities. Here, we focus on Mediterranean wetlands in central Iberia and perform a multi‐level, comparative study of two endemic pond‐breeding amphibians, a salamander (Pleurodeles waltl) and a toad (Pelobates cultripes). We genotyped 411 salamanders from 20 populations and 306 toads from 16 populations at 18 and 16 microsatellite loci, respectively, and identified major factors associated with population connectivity through the analysis of three sets of variables potentially affecting gene flow at increasingly finer levels of spatial resolution. Topographic, land use/cover, and remotely sensed vegetation/moisture indices were used to derive optimized resistance surfaces for the two species. We found contrasting patterns of genetic structure, with stronger, finer scale genetic differentiation in Pleurodeles waltl, and notable differences in the role of fine‐scale patterns of heterogeneity in vegetation cover and water content in shaping patterns of regional genetic structure in the two species. Overall, our results suggest a positive role of structural heterogeneity in population connectivity in pond‐breeding amphibians, with habitat patches of Mediterranean scrubland and open oak woodlands (“dehesas”) facilitating gene flow. Our study highlights the usefulness of remotely sensed continuous variables of land cover, vegetation and water content (e.g., NDVI, NDMI) in conservation‐oriented studies aimed at identifying major drivers of population connectivity.     

Predicting bird phenology from space: satellite‐derived vegetation green‐up signal uncovers spatial variation in phenological synchrony between birds and their environment

Population‐level studies of how tit species (Parus spp.) track the changing phenology of their caterpillar food source have provided a model system allowing inference into how populations can adjust to changing climates, but are often limited because they implicitly assume all individuals experience similar environments. Ecologists are increasingly using satellite‐derived data to quantify aspects of animals' environments, but so far studies examining phenology have generally done so at large spatial scales. Considering the scale at which individuals experience their environment is likely to be key if we are to understand the ecological and evolutionary processes acting on reproductive phenology within populations. Here, we use time series of satellite images, with a resolution of 240 m, to quantify spatial variation in vegetation green‐up for a 385‐ha mixed‐deciduous woodland. Using data spanning 13 years, we demonstrate that annual population‐level measures of the timing of peak abundance of winter moth larvae (Operophtera brumata) and the timing of egg laying in great tits (Parus major) and blue tits (Cyanistes caeruleus) is related to satellite‐derived spring vegetation phenology. We go on to show that timing of local vegetation green‐up significantly explained individual differences in tit reproductive phenology within the population, and that the degree of synchrony between bird and vegetation phenology showed marked spatial variation across the woodland. Areas of high oak tree (Quercus robur) and hazel (Corylus avellana) density showed the strongest match between remote‐sensed vegetation phenology and reproductive phenology in both species. Marked within‐population variation in the extent to which phenology of different trophic levels match suggests that more attention should be given to small‐scale processes when exploring the causes and consequences of phenological matching. We discuss how use of remotely sensed data to study within‐population variation could broaden the scale and scope of studies exploring phenological synchrony between organisms and their environment.     

Listeria monocytogenes induces IFNβ expression through an IFI16‐, cGAS‐ and STING‐dependent pathway

Listeria monocytogenes is a gram‐positive facultative intracellular bacterium, which replicates in the cytoplasm of myeloid cells. Interferon β (IFNβ) has been reported to play an important role in the mechanisms underlying Listeria disease. Although studies in murine cells have proposed the bacteria‐derived cyclic‐di‐AMP to be the key bacterial immunostimulatory molecule, the mechanism for IFNβ expression during L. monocytogenes infection in human myeloid cells remains unknown. Here we report that in human macrophages, Listeria DNA rather than cyclic‐di‐AMP is stimulating the IFN response via a pathway dependent on the DNA sensors IFI16 and cGAS as well as the signalling adaptor molecule STING. Thus, Listeria DNA is a major trigger of IFNβ expression in human myeloid cells and is sensed to activate a pathway dependent on IFI16, cGAS and STING.     

Diffuse reflectance spectroscopy in Barrett’s Esophagus: developing a large field‐of‐view screening method discriminating dysplasia from metaplasia

We evaluated diffuse reflectance spectroscopy implemented as a small field‐of‐view technique for discrimination of dysplasia from metaplasia in Barrett’s esophagus as an adjuvant to autofluorescence endoscopy. Using linear discriminant analysis on 2579 spectra measured in 54 patients identified an optimum a 4‐wavelength classifier (at 485, 513, 598 and 629 nm). Sensitivity and specificity for a test data set were 0.67 and 0.85, respectively. Spectroscopic results show that this technique could be implemented in wide‐field imaging mode to improve the accuracy of existing endoscopy techniques for finding early pre‐malignant lesions in Barrett’s esophagus. Results show that the discrimination occurs likely due to redistribution of blood content in the tissue sensed by the optical probing with the wavelength‐dependent sampling depth. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Temporal transferability of wildlife habitat models: implications for habitat monitoring

Aim Temporal transferability is an important issue when habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of habitat monitoring. While the combination of remote sensing technology and habitat modelling provides a useful tool for habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite‐derived variables affect temporal transferability of habitat models and their usefulness for habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single‐year or multi‐year (i.e. 3‐year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda habitat within and across time periods by using threshold‐independent and threshold‐dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda habitat. However, the models developed using multi‐year data exhibited significantly higher temporal transferability than those developed using single‐year data. In addition, models developed with phenology metrics, especially when using multi‐year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with habitat modelling constitutes a suitable tool for characterizing wildlife habitat and monitoring its temporal dynamics. Using multi‐year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter‐annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for habitat monitoring through the integration of remote sensing technology and habitat modelling, which may be useful for the conservation of the giant panda and many other species.     

Estimation of Mediterranean forest transpiration and photosynthesis through the use of an ecosystem simulation model driven by remotely sensed data

Aim This paper investigates the use of an ecosystem simulation model, FOREST‐BGC, to estimate the main ecophysiological processes (transpiration and photosynthesis) of Mediterranean coastal forest areas using remotely sensed data. Location Model testing was carried out at two protected forest sites in central Italy, one of which was covered by Turkey oak (Circeo National Park) and the other by holm‐oak (Castelporziano Estate). Methods At both sites, transpiration and photosynthesis measurements were collected in the field during the growing seasons over a four‐year period (1999 and 2001 for the Turkey oak; 1997, 1999 and 2000 for the holm‐oak). Calibration of the model was obtained through combining information derived from ground measurements and remotely sensed data. In particular, remote sensing estimates of the Leaf Area Index derived from 1 × 1‐km NOAA AVHRR Normalized Difference Vegetation Index data were used to improve the adaptation of the model to local forest conditions. Results The results indicated different strategies regarding water use efficiency, ‘water spending’ for Turkey oak and ‘water saving’ for holm‐oak. The water use efficiency for the holm‐oak was consistently higher than that for the Turkey oak and the relationship between VPD and WUE for the holm‐oak showed a higher coefficient of determination (R² = 0.9238). Comparisons made between the field measurements of transpiration and photosynthesis and the model estimates showed that the integration procedure used for the deciduous oak forest was effective, but that there is a need for further studies regarding the sclerophyllous evergreen forest. In particular, for Turkey oak the simulations of transpiration yielded very good results, with errors lower than 0.3 mm H₂O/day, while the simulation accuracy for photosynthesis was lower. In the case of holm‐oak, transpiration was markedly overestimated for all days considered, while the simulations of photosynthesis were very accurate. Main conclusions Overall, the approach offers interesting operational possibilities for the monitoring of Mediterranean forest ecosystems, particularly in view of the availability of new satellite sensors with a higher spatial and temporal resolution, which have been launched in recent years.     

A multiscale analysis of canopy structure in Fagus sylvatica L. and Quercus cerris L. old‐growth forests in the Cilento and Vallo di Diano National Park

Abstract

Broadleaved forest is one of the most severely exploited and threatened ecosystems worldwide such that many authors have highlighted the scarcity of undisturbed old‐growth broadleaved forests, especially in southern Europe. From an ecological perspective, old‐growth forests are considered to be significant for their structural diversity and complex ecological relationships among species. In this paper, we compare ground measurements of leaf area index (LAI) and the remotely sensed normalized difference vegetation index (NDVI) autocorrelation pattern of two old‐growth forest stands in the Cilento National Park (southern Italy) with two nearby managed forests stands of the same type. Results show that old‐growth forests have higher fine‐scale variability in both LAI and NDVI values and longer autocorrelation ranges than the corresponding managed forests. The potential relevance of these findings for the single large or several small (SLOSS) debate is also briefly discussed.     

Satellite data identify decadal trends in the quality of Pygoscelis penguin chick‐rearing habitat

Pygoscelis penguins are experiencing general population declines in their northernmost range whereas there are reported increases in their southernmost range. These changes are coincident with decadal‐scale trends in remote sensed observations of sea ice concentrations (SIC) and sea surface temperatures (SST) during the chick‐rearing season (austral summer). Using SIC, SST, and bathymetry, we identified separate chick‐rearing niche spaces for the three Pygoscelis penguin species and used a maximum entropy approach (MaxEnt) to spatially and temporally model suitable chick‐rearing habitats in the Southern Ocean. For all Pygoscelis penguin species, the MaxEnt models predict significant changes in the locations of suitable chick‐rearing habitats over the period of 1982–2010. In general, chick‐rearing habitat suitability at specific colony locations agreed with the corresponding increases or decreases in documented population trends over the same time period. These changes were the most pronounced along the West Antarctic Peninsula where there has been a rapid warming event during at least the last 50 years.     

Sensitivity of primary production to precipitation across the United States

Primary production, a key regulator of the global carbon cycle, is highly responsive to variations in climate. Yet, a detailed, continental‐scale risk assessment of climate‐related impacts on primary production is lacking. We combined 16 years of MODIS NDVI data, a remotely sensed proxy for primary production, with observations from 1218 climate stations to derive values of ecosystem sensitivity to precipitation and aridity. For the first time, we produced an empirically‐derived map of ecosystem sensitivity to climate across the conterminous United States. Over this 16‐year period, annual primary production values were most sensitive to precipitation and aridity in dryland and grassland ecosystems. Century‐long trends measured at the climate stations showed intensifying aridity and climatic variability in many of these sensitive regions. Dryland ecosystems in the western US may be particularly vulnerable to reductions in primary production and consequent degradation of ecosystem services as climate change and variability increase in the future.     

Why is the South Orkney Island shelf (the world's first high seas marine protected area) a carbon immobilization hotspot?

The Southern Ocean archipelago, the South Orkney Islands (SOI), became the world's first entirely high seas marine protected area (MPA) in 2010. The SOI continental shelf (~44 000 km²), was less than half covered by grounded ice sheet during glaciations, is biologically rich and a key area of both sea surface warming and sea‐ice losses. Little was known of the carbon cycle there, but recent work showed it was a very important site of carbon immobilization (net annual carbon accumulation) by benthos, one of the few demonstrable negative feedbacks to climate change. Carbon immobilization by SOI bryozoans was higher, per species, unit area and ice‐free day, than anywhere‐else polar. Here, we investigate why carbon immobilization has been so high at SOI, and whether this is due to high density, longevity or high annual production in six study species of bryozoans (benthic suspension feeders). We compared benthic carbon immobilization across major regions around West Antarctica with sea‐ice and primary production, from remotely sensed and directly sampled sources. Lowest carbon immobilization was at the northernmost study regions (South Georgia) and southernmost Amundsen Sea. However, data standardized for age and density showed that only SOI was anomalous (high). High immobilization at SOI was due to very high annual production of bryozoans (rather than high densities or longevity), which were 2x, 3x and 5x higher than on the Bellingshausen, South Georgia and Amundsen shelves, respectively. We found that carbon immobilization correlated to the duration (but not peak or integrated biomass) of phytoplankton blooms, both in directly sampled, local scale data and across regions using remote‐sensed data. The long bloom at SOI seems to drive considerable carbon immobilization, but sea‐ice losses across West Antarctica mean that significant carbon sinks and negative feedbacks to climate change could also develop in the Bellingshausen and Amundsen seas.     

MODISTools – downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate‐resolution Imaging Spectroradiometer (MODIS), providing twice‐daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per‐location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta‐analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time‐series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R² values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub ( https://github.com/seantuck12/MODISTools ).     

The remote sensing approach in broad‐scale phenological studies

Abstract. Satellite imagery provides a unique tool for monitoring seasonal dynamics of the Earth's vegetation on a global scale. The combination of the normalized difference vegetation index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) with a daily repeat cycle and 1 km spatial resolution makes weather satellites operated by the National Oceanic and Atmospheric Administration very well suited for deriving broad‐scale phenological metrics from satellite images. In this paper, similarities and differences between remotely sensed phenological studies and traditional symphenological studies conducted by ground‐based observations are summarized. Finally, major shortcomings in deriving phenological metrics from NDVI time series are discussed.     

Simulating Regional Vegetation‐climate Dynamics for Middle America: Tropical Versus Temperate Applications

Regional vegetation‐climate modelling studies have typically focused on boreal or temperate ecosystems in North America and Europe, almost completely overlooking tropical ecosystems. We present the first results of simulated regional vegetation‐climate dynamics in Middle America as simulated by the model, LPJ‐GUESS. The Kappa statistic indicated poor agreement, with a Kappa value of 0.301. When we modified the Kappa statistic by aggregating cell sizes and using generalized biomes, the Kappa value increased to 0.543, indicating a fair agreement. Total LAI simulated from LPJ‐GUESS was strongly correlated to remotely sensed LAI values (r = 0.75). Our simulations indicate that fire frequency was overestimated in tropical moist forests and underestimated in savannas. This underestimation of fire resulted in an over‐simulation of dry tropical forest at the expense of savanna. We highlight additional reasons for the initially poor representation of vegetation in Middle America, including factors such as non‐parameterized plant functional types (desert shrub, cacti, and other succulents), rugged topography, and an insufficient representation of soil.     

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.