Three-dimensional species distribution modelling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs.

The three-dimensional structure of habitats is a critical component of species' niches driving coexistence in species-rich ecosystems. However, its influence on structuring and partitioning recruitment niches has not been widely addressed. We developed a new method to combine species distribution modelling and structure from motion, and characterized three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and gorgonians. Fine-scale roughness was the most important predictor of suitable habitat for both taxa, and their niches largely overlapped, primarily due to scleractinians' broader niche breadth. Crevices and holes at mm scales on calcareous rock with low coral cover were more suitable for octocorals than for scleractinian recruits, suggesting that the decline in scleractinian corals is facilitating the recruitment of octocorals on contemporary Caribbean reefs. However, the relative abundances of the taxa were independent of the amount of suitable habitat on the reef, emphasizing that niche processes alone do not predict recruitment rates.

Support for a relationship between demography and modeled habitat suitability is scale dependent for the purple martin Progne subis.

Species distribution models (SDMs) estimate habitat suitability for species in geographic space. They are extensively used in conservation under the assumption that there is a positive relationship between habitat suitability and species success and stability. Given the difficulties in obtaining demographic data across a species' range, this assumption is rarely tested. Here we provide a range-wide test of this relationship for the eastern subspecies of purple martin Progne subis subis. We build a well-supported SDM for the breeding range of the purple martin, and pair it with an unparalleled demographic dataset of nest success and local and regional abundance data for the species to test the proposed link between habitat suitability and fecundity and demography. We find a positive relationship between regional abundance and habitat suitability but no relationship between local abundance or fecundity and habitat suitability. Our data suggest that local success is driven largely by biotic and stochastic factors and raise the possibility that purple martins are experiencing a time lag in their distribution. More broadly our results call for caution in how we interpret SDMs and do not support the assumption that areas of high habitat suitability are the best areas for species persistence.

Intensifying postfire weather and biological invasion drive species loss in a Mediterranean-type biodiversity hotspot.

Prolonged periods of extreme heat or drought in the first year after fire affect the resilience and diversity of fire-dependent ecosystems by inhibiting seed germination or increasing mortality of seedlings and resprouting individuals. This interaction between weather and fire is of growing concern as climate changes, particularly in systems subject to stand-replacing crown fires, such as most Mediterranean-type ecosystems. We examined the longest running set of permanent vegetation plots in the Fynbos of South Africa (44 y), finding a significant decline in the diversity of plots driven by increasingly severe postfire summer weather events (number of consecutive days with high temperatures and no rain) and legacy effects of historical woody alien plant densities 30 y after clearing. Species that resprout after fire and/or have graminoid or herb growth forms were particularly affected by postfire weather, whereas all species were sensitive to invasive plants. Observed differences in the response of functional types to extreme postfire weather could drive major shifts in ecosystem structure and function such as altered fire behavior, hydrology, and carbon storage. An estimated 0.5 °C increase in maximum temperature tolerance of the species sets unique to each survey further suggests selection for species adapted to hotter conditions. Taken together, our results show climate change impacts on biodiversity in the hyperdiverse Cape Floristic Region and demonstrate an important interaction between extreme weather and disturbance by fire that may make flammable ecosystems particularly sensitive to climate change.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

Towards connecting biodiversity and geodiversity across scales with satellite remote sensing.

ISSUE: Geodiversity (i.e., the variation in Earth's abiotic processes and features) has strong effects on biodiversity patterns. However, major gaps remain in our understanding of how relationships between biodiversity and geodiversity vary over space and time. Biodiversity data are globally sparse and concentrated in particular regions. In contrast, many forms of geodiversity can be measured continuously across the globe with satellite remote sensing. Satellite remote sensing directly measures environmental variables with grain sizes as small as tens of metres and can therefore elucidate biodiversity-geodiversity relationships across scales. EVIDENCE: We show how one important geodiversity variable, elevation, relates to alpha, beta and gamma taxonomic diversity of trees across spatial scales. We use elevation from NASA's Shuttle Radar Topography Mission (SRTM) and c. 16,000 Forest Inventory and Analysis plots to quantify spatial scaling relationships between biodiversity and geodiversity with generalized linear models (for alpha and gamma diversity) and beta regression (for beta diversity) across five spatial grains ranging from 5 to 100 km. We illustrate different relationships depending on the form of diversity; beta and gamma diversity show the strongest relationship with variation in elevation. CONCLUSION: With the onset of climate change, it is more important than ever to examine geodiversity for its potential to foster biodiversity. Widely available satellite remotely sensed geodiversity data offer an important and expanding suite of measurements for understanding and predicting changes in different forms of biodiversity across scales. Interdisciplinary research teams spanning biodiversity, geoscience and remote sensing are well poised to advance understanding of biodiversity-geodiversity relationships across scales and guide the conservation of nature.

Factors affecting MeHg bioaccumulation in stream biota: the role of dissolved organic carbon and diet.

The bioaccumulation of the neurotoxin methylmercury (MeHg) in freshwater ecosystems is thought to be mediated by both water chemistry (e.g., dissolved organic carbon [DOC] and dissolved mercury [Hg]) and diet (e.g., trophic position and diet composition). Hg in small streams is of particular interest given their role as a link between terrestrial and aquatic processes. Terrestrial processes determine the quantity and quality of streamwater DOC, which in turn influence the quantity and bioavailability of dissolved MeHg. To better understand the effects of water chemistry and diet on Hg bioaccumulation in stream biota, we measured DOC and dissolved Hg in stream water and mercury concentration in three benthic invertebrate taxa and three fish species across up to 12 tributary streams in a forested watershed in New Hampshire, USA. As expected, dissolved total mercury (THg) and MeHg concentrations increased linearly with DOC. However, mercury concentrations in fish and invertebrates varied non-linearly, with maximum bioaccumulation at intermediate DOC concentrations, which suggests that MeHg bioavailability may be reduced at high levels of DOC. Further, MeHg and THg concentrations in invertebrates and fish, respectively, increased with δ15N (suggesting trophic position) but were not associated with δ13C. These results show that even though MeHg in water is strongly determined by DOC concentrations, mercury bioaccumulation in stream food webs is the result of both MeHg availability in stream water and trophic position.

Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa.

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.

Modeling daily flowering probabilities: expected impact of climate change on Japanese cherry phenology.

Understanding the drivers of phenological events is vital for forecasting species' responses to climate change. We developed flexible Bayesian survival regression models to assess a 29-year, individual-level time series of flowering phenology from four taxa of Japanese cherry trees (Prunus spachiana, Prunus × yedoensis, Prunus jamasakura, and Prunus lannesiana), from the Tama Forest Cherry Preservation Garden in Hachioji, Japan. Our modeling framework used time-varying (chill and heat units) and time-invariant (slope, aspect, and elevation) factors. We found limited differences among taxa in sensitivity to chill, but earlier flowering taxa, such as P. spachiana, were more sensitive to heat than later flowering taxa, such as P. lannesiana. Using an ensemble of three downscaled regional climate models under the A1B emissions scenario, we projected shifts in flowering timing by 2100. Projections suggest that each taxa will flower about 30 days earlier on average by 2100 with 2-6 days greater uncertainty around the species mean flowering date. Dramatic shifts in the flowering times of cherry trees may have implications for economically important cultural festivals in Japan and East Asia. The survival models used here provide a mechanistic modeling approach and are broadly applicable to any time-to-event phenological data, such as plant leafing, bird arrival time, and insect emergence. The ability to explicitly quantify uncertainty, examine phenological responses on a fine time scale, and incorporate conditions leading up to an event may provide future insight into phenologically driven changes in carbon balance and ecological mismatches of plants and pollinators in natural populations and horticultural crops.

Integrating remote sensing with ecology and evolution to advance biodiversity conservation.

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.

Developing dynamic mechanistic species distribution models: predicting bird-mediated spread of invasive plants across northeastern North America.

Species distribution models are a fundamental tool in ecology, conservation biology, and biogeography and typically identify potential species distributions using static phenomenological models. We demonstrate the importance of complementing these popular models with spatially explicit, dynamic mechanistic models that link potential and realized distributions. We develop general grid-based, pattern-oriented spread models incorporating three mechanisms--plant population growth, local dispersal, and long-distance dispersal--to predict broadscale spread patterns in heterogeneous landscapes. We use the model to examine the spread of the invasive Celastrus orbiculatus (Oriental bittersweet) by Sturnus vulgaris (European starling) across northeastern North America. We find excellent quantitative agreement with historical spread records over the last century that are critically linked to the geometry of heterogeneous landscapes and each of the explanatory mechanisms considered. Spread of bittersweet before 1960 was primarily driven by high growth rates in developed and agricultural landscapes, while subsequent spread was mediated by expansion into deciduous and coniferous forests. Large, continuous patches of coniferous forests may substantially impede invasion. The success of C. orbiculatus and its potential mutualism with S. vulgaris suggest troubling predictions for the spread of other invasive, fleshy-fruited plant species across northeastern North America.

Global daily 1 km land surface precipitation based on cloud cover-informed downscaling.

High-resolution climatic data are essential to many questions and applications in environmental research and ecology. Here we develop and implement a new semi-mechanistic downscaling approach for daily precipitation estimate that incorporates high resolution (30 arcsec, ≈1 km) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1 km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data from the Global Historical Climate Network indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from the continental United States further indicates that CHELSA-EarthEnv performs well in comparison to other precipitation products. The CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared with a large improvement in the spatial accuracy especially in complex terrain.

The geodiv r package: Tools for calculating gradient surface metrics.

The geodiv r package calculates gradient surface metrics from imagery and other gridded datasets to provide continuous measures of landscape heterogeneity for landscape pattern analysis. geodiv is the first open-source, command line toolbox for calculating many gradient surface metrics and easily integrates parallel computing for applications with large images or rasters (e.g. remotely sensed data). All functions may be applied either globally to derive a single metric for an entire image or locally to create a texture image over moving windows of a user-defined extent.We present a comprehensive description of the functions available through geodiv. A supplemental vignette provides an example application of geodiv to the fields of landscape ecology and biogeography. geodiv allows users to easily retrieve estimates of spatial heterogeneity for a variety of purposes, enhancing our understanding of how environmental structure influences ecosystem processes. The package works with any continuous imagery and may be widely applied in many fields where estimates of surface complexity are useful.

Multivariate forecasts of potential distributions of invasive plant species.

The fact that plant invasions are an ongoing process makes generalizations of invasive spread extraordinarily challenging. This is particularly true given the idiosyncratic nature of invasions, in which both historical and local conditions affect establishment success and hinder our ability to generate guidelines for early detection and eradication of invasive species. To overcome these limitations we have implemented a comprehensive approach that examines plant invasions at three spatial scales: regional, landscape, and local levels. At each scale, in combination with the others, we have evaluated the role of key environmental variables such as climate, landscape structure, habitat type, and canopy closure in the spread of three commonly found invasive woody plant species in New England, Berberis thunbergii, Celastrus orbiculatus, and Euonymus alatus. We developed a spatially explicit hierarchical Bayesian model that allowed us to take into account the ongoing nature of the spread of invasive species and to incorporate presence/absence data from the species' native ranges as well as from the invaded regions. Comparisons between predictions from climate-only models with those from the multiscale forecasts emphasize the importance of including landscape structure in our models of invasive species' potential distributions. In addition, predictions generated using only native range data performed substantially worse than those that incorporated data from the target range. This points out important limitations in extrapolating distributional ranges from one region to another.

Metabolic asymmetry and the global diversity of marine predators.

Species richness of marine mammals and birds is highest in cold, temperate seas-a conspicuous exception to the general latitudinal gradient of decreasing diversity from the tropics to the poles. We compiled a comprehensive dataset for 998 species of sharks, fish, reptiles, mammals, and birds to identify and quantify inverse latitudinal gradients in diversity, and derived a theory to explain these patterns. We found that richness, phylogenetic diversity, and abundance of marine predators diverge systematically with thermoregulatory strategy and water temperature, reflecting metabolic differences between endotherms and ectotherms that drive trophic and competitive interactions. Spatial patterns of foraging support theoretical predictions, with total prey consumption by mammals increasing by a factor of 80 from the equator to the poles after controlling for productivity.

Content Volatility of Scientific Topics in Wikipedia: A Cautionary Tale.

Wikipedia has quickly become one of the most frequently accessed encyclopedic references, despite the ease with which content can be changed and the potential for 'edit wars' surrounding controversial topics. Little is known about how this potential for controversy affects the accuracy and stability of information on scientific topics, especially those with associated political controversy. Here we present an analysis of the Wikipedia edit histories for seven scientific articles and show that topics we consider politically but not scientifically "controversial" (such as evolution and global warming) experience more frequent edits with more words changed per day than pages we consider "noncontroversial" (such as the standard model in physics or heliocentrism). For example, over the period we analyzed, the global warming page was edited on average (geometric mean ±SD) 1.9±2.7 times resulting in 110.9±10.3 words changed per day, while the standard model in physics was only edited 0.2±1.4 times resulting in 9.4±5.0 words changed per day. The high rate of change observed in these pages makes it difficult for experts to monitor accuracy and contribute time-consuming corrections, to the possible detriment of scientific accuracy. As our society turns to Wikipedia as a primary source of scientific information, it is vital we read it critically and with the understanding that the content is dynamic and vulnerable to vandalism and other shenanigans.

Air pollution and the demand for hospital services: a review.

Time-series studies published since 1993 on the association between short-term changes in air quality and use of hospital services, including both inpatient and emergency room use, are reviewed. The use of nonparametric analysis, often incorporating generalized additive models (GAMs), has increased greatly since the early 1990s. There have also been three major multi-city studies, which together analyzed data from well over 100 cities in Europe and North America. Various air pollutants, especially ozone (O(3)), particulate matter (PM), nitrogen dioxide (NO(2)) and sulfur dioxide (SO(2)), were generally found to be significantly associated with increased use of hospital services. Ozone tends to have stronger effects in the summer during periods of higher concentrations. Several studies revealed synergistic effects between pollutants such as PM and SO(2). Overall, short-term exposure to air pollutants is found to be an important predictor of increased hospital and emergency room use around the world.

Air pollution, weather, and respiratory emergency room visits in two northern New England cities: an ecological time-series study.

Daily emergency room (ER) visits for all respiratory (ICD-9 460-519) and asthma (ICD-9 493) were compared with daily sulfur dioxide (SO2), ozone (O3), and weather variables over the period 1998-2000 in Portland, Maine (population 248,000), and 1996-2000 in Manchester, New Hampshire (population 176,000). Seasonal variability was removed from all variables using nonparametric smoothed function (LOESS) of day of study. Generalized additive models were used to estimate the effect of elevated levels of pollutants on ER visits. Relative risks of pollutants are reported over their interquartile range (IQR, the 75th -25th percentile pollutant values). In Portland, an IQR increase in SO2 was associated with a 5% (95% CI 2-7%) increase in all respiratory ER visits and a 6% (95% CI 1-12%) increase in asthma visits. An IQR increase in O3 was associated with a 5% (95% CI 1-10%) increase in Portland asthmatic ER visits. No significant associations were found in Manchester, New Hampshire, possibly due to statistical limitations of analyzing a smaller population. The absence of statistical evidence for a relationship should not be used as evidence of no relationship. This analysis reveals that, on a daily basis, elevated SO2 and O3 have a significant impact on public health in Portland, Maine.