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This paper examined the relationship between birth weight, precipitation, and temperature in 19 African countries. We matched recorded birth weights from Demographic and Health Surveys covering 1986 through 2010 with gridded monthly precipitation and temperature data derived from satellite and ground-based weather stations. Observed weather patterns during various stages of pregnancy were also used to examine the effect of temperature and precipitation on birth weight outcomes. In our empirical model we allowed the effect of weather factors to vary by the dominant food production strategy (livelihood zone) in a given region as well as by household wealth, mother's education and birth season. This allowed us to determine if certain populations are more or less vulnerable to unexpected weather changes after adjusting for known covariates. Finally we measured effect size by observing differences in birth weight outcomes in women who have one low birth weight experience and at least one healthy birth weight baby. The results indicated that climate does indeed impact birth weight and at a level comparable, in some cases, to the impact of increasing women's education or household electricity status.

Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

Southwest Asia, defined as the region containing the countries of Afghanistan, Iran, Iraq, and Pakistan, is water scarce and receives nearly 75% of its annual rainfall during the boreal cold season of November–April. The forcing of southwest Asia precipitation has been previously examined for the entire boreal cold season from the perspective of climate variability originating over the Atlantic and tropical Indo-Pacific Oceans. This study examines the intermonthly differences in precipitation variability over southwest Asia and the atmospheric conditions directly responsible in forcing monthly November–April precipitation. Seasonally averaged November–April precipitation over southwest Asia is significantly correlated with sea surface temperature (SST) patterns consistent with Pacific decadal variability (PDV), El Niño–Southern Oscillation (ENSO), and the long-term change of global SST (LT). In contrast, the precipitation variability during the individual months of November–April is unrelated and is correlated with SST signatures that include PDV, ENSO, and LT in different combinations. Despite strong intermonthly differences in precipitation variability during November–April over southwest Asia, similar atmospheric circulations, highlighted by a stationary equivalent barotropic Rossby wave centered over Iraq, force the monthly spatial distributions of precipitation. Tropospheric flow on the eastern side of the equivalent barotropic Rossby wave modifies the flux of moisture and advects the mean meridional temperature gradient, resulting in temperature advection that is balanced by vertical motions over southwest Asia. The forcing of monthly southwest Asia precipitation by equivalent barotropic Rossby waves is different from the forcing by baroclinic Rossby waves associated with tropically forced–only modes of climate variability.

Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.

Amphibian species persisting in isolated streams and wetlands in desert environments can be susceptible to low connectivity, genetic isolation, and climate changes. We evaluated the past (1900–1930), recent (1981–2010), and future (2071–2100) climate suitability of the arid Great Basin (USA) for the Columbia spotted frog (Rana luteiventris) and assessed whether changes in surface water may affect connectivity for remaining populations. We developed a predictive model of current climate suitability and used it to predict the historic and future distribution of suitable climates. We then modeled changes in surface water availability at each time period. Finally, we quantified connectivity among existing populations on the basis of hydrology and correlated it with interpopulation genetic distance. We found that the area of the Great Basin with suitable climate conditions has declined by approximately 49% over the last century and will likely continue to decline under future climate scenarios. Climate conditions at currently occupied locations have been relatively stable over the last century, which may explain persistence at these sites. However, future climates at these currently occupied locations are predicted to become warmer throughout the year and drier during the frog's activity period (May – September). Fall and winter precipitation may increase, but as rain instead of snow. Earlier runoff and lower summer base flows may reduce connectivity between neighboring populations, which is already limited. Many of these changes could have negative effects on remaining populations over the next 50–80 years, but milder winters, longer growing seasons, and wetter falls might positively affect survival and dispersal. Collectively, however, seasonal shifts in temperature, precipitation, and stream flow patterns could reduce habitat suitability and connectivity for frogs and possibly other aquatic species inhabiting streams in this arid region.

©2015. American Geophysical Union. California is experiencing one of the worst droughts on record. We use a hydrological model and risk assessment framework to understand the influence of temperature on the water year (WY) 2014 drought in California and examine the probability that this drought would have been less severe if temperatures resembled the historical climatology. Our results indicate that temperature played an important role in exacerbating the WY 2014 drought severity. We found that if WY 2014 temperatures resembled the 1916-2012 climatology, there would have been at least an 86% chance that winter snow water equivalent and spring-summer soil moisture and runoff deficits would have been less severe than the observed conditions. We also report that the temperature forecast skill in California for the important seasons of winter and spring is negligible, beyond a lead time of 1month, which we postulate might hinder skillful drought prediction in California.

SSTs in the western Pacific Ocean have tracked closely with CMIP5 simulations despite recent hiatus cooling in the eastern Pacific. This paper quantifies these similarities and associated circulation and precipitation variations using the first global 1900–2012 ENSO-residual empirical orthogonal functions (EOFs) of 35 variables: observed SSTs; 28 CMIP5 SST simulations; Simple Ocean Data Assimilation (SODA) 25-, 70-, and 171-m ocean temperatures and sea surface heights (SSHs); and Twentieth Century Reanalysis, version 2 (20CRv2), surface winds and precipitation. While estimated independently, these leading EOFs across all variables fit together in a meaningful way, and the authors refer to them jointly as the west Pacific warming mode (WPWM). WPWM SST EOFs correspond closely in space and time. Their spatial patterns form a “western V” extending from the Maritime Continent into the extratropical Pacific. Their temporal principal components (PCs) have increased rapidly since 1990; this increase has been primarily due to radiative forcing and not natural decadal variability. WPWM circulation changes appear consistent with a Matsuno–Gill-like atmospheric response associated with an ocean–atmosphere dipole structure contrasting increased (decreased) western (eastern) Pacific precipitation, SSHs, and ocean temperatures. These changes have enhanced the Walker circulation and modulated weather on a global scale. An AGCM experiment and the WPWM of global boreal spring precipitation indicate significant drying across parts of East Africa, the Middle East, the southwestern United States, southern South America, and Asia. Changes in the WPWM have tracked closely with precipitation and the increase in drought frequency over the semiarid and water-insecure areas of East Africa, the Middle East, and southwest Asia.

Genomics has great potential to advance understanding of amphibian evolution, ecology, and behavior, as well as to improve conservation of this highly imperiled class of vertebrates. However, application of new massively parallel sequencing technology to amphibians lags behind its application to other vertebrates, due in part to their large, repetitive genomes, making genome assembly challenging. The goal of our chapter is to outline ways in which population genomics – coupled with field biology, experiments, and modeling – can deepen our understanding of basic and applied questions in amphibian evolutionary ecology and conservation. We start by discussing potential applications of genomics to several long-standing questions in amphibian evolution, ecology, and behavior, including phylogenetic relationships, phylogeography, sex chromosome evolution, population structure and demography, local adaptation, and mating systems and sexual selection. We then highlight opportunities for improving amphibian conservation with genomics, focusing on hybridization, disease evolution and ecology, and captive breeding programs. Next, we provide strategies for moving amphibian genomics forward in the face of challenges such as few available reference genomes and large repetitive genomes, including a bold proposal for whole genome sequencing of a minimum of one species per amphibian family. We conclude by providing suggestions for maximizing the potential of genomics to advance understanding of amphibian evolutionary ecology and conservation and recommendations for getting started in genomics.

Physical barriers to gene flow were once viewed as prerequisites for adaptive evolutionary divergence. However, a growing body of theoretical and empirical work suggests that divergence can proceed within a single population. Here we document genetic structure and spatially replicated patterns of phenotypic divergence within a bird species endemic to 250 km(2) Santa Cruz Island, California, USA. Island scrub-jays (Aphelocoma insularis) in three separate stands of pine habitat had longer, shallower bills than jays in oak habitat, a pattern that mirrors adaptive differences between allopatric populations of the species' mainland congener. Variation in both bill measurements was heritable, and island scrub-jays mated nonrandomly with respect to bill morphology. The population was not panmictic; instead, we found a continuous pattern of isolation by distance across the east-west axis of the island, as well as a subtle genetic discontinuity across the boundary between the largest pine stand and adjacent oak habitat. The ecological factors that appear to have facilitated adaptive differentiation at such a fine scale--environmental heterogeneity and localized dispersal--are ubiquitous in nature. These findings support recent arguments that microgeographic patterns of adaptive divergence may be more common than currently appreciated, even in mobile taxonomic groups like birds.

Southwestern Asia, defined here as the domain bounded by 20°–40°N and 40°–70°E, which includes the nations of Iraq, Iran, Afghanistan, and Pakistan, is a water-stressed and semiarid region that receives roughly 75% of its annual rainfall during November–April. The November–April climate of southwestern Asia is strongly influenced by tropical Indo-Pacific variability on intraseasonal and interannual time scales, much of which can be attributed to sea surface temperature (SST) variations. The influences of lower-frequency SST variability on southwestern Asia climate during November–April Pacific decadal SST (PDSST) variability and the long-term trend in SST (LTSST) is examined. The U.S. Climate Variability and Predictability Program (CLIVAR) Drought Working Group forced global atmospheric climate models with PDSST and LTSST patterns, identified using empirical orthogonal functions, to show the steady atmospheric response to these modes of decadal to multidecadal SST variability. During November–April, LTSST forces an anticyclone over southwestern Asia, which results in reduced precipitation and increases in surface temperature. The precipitation and tropospheric circulation influences of LTSST are corroborated by independent observed precipitation and circulation datasets during 1901–2004. The decadal variations of southwestern Asia precipitation may be forced by PDSST variability, with two of the three models indicating that the cold phase of PDSST forces an anticyclone and precipitation reductions. However, there are intermodel circulation variations to PDSST that influence subregional precipitation patterns over the Middle East, southwestern Asia, and subtropical Asia. Changes in wintertime temperature and precipitation over southwestern Asia forced by LTSST and PDSST imply important changes to the land surface hydrology during the spring and summer.

Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the “true” observed precipitation value at each grid cell.

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

Genetic rescue can increase the fitness of small, imperiled populations via immigration. A suite of studies from the past decade highlights the value of genetic rescue in increasing population fitness. Nonetheless, genetic rescue has not been widely applied to conserve many of the threatened populations that it could benefit. In this review, we highlight recent studies of genetic rescue and place it in the larger context of theoretical and empirical developments in evolutionary and conservation biology. We also propose directions to help shape future research on genetic rescue. Genetic rescue is a tool that can stem biodiversity loss more than has been appreciated, provides population resilience, and will become increasingly useful if integrated with molecular advances in population genomics.

The ecological and evolutionary consequences of extreme events are poorly understood. Here, we tested predictions about species persistence and population genomic change in aquatic insects in 14 Colorado mountain streams across a hydrological disturbance gradient caused by a one in 500-year rainfall event. Taxa persistence ranged from 39 to 77% across sites and declined with increasing disturbance in relation to species' resistance and resilience traits. For taxa with mobile larvae and terrestrial adult stages present at the time of the flood, average persistence was 84% compared to 25% for immobile taxa that lacked terrestrial adults. For two of six species analysed, genomic diversity (allelic richness) declined after the event. For one species it greatly expanded, suggesting resilience via re-colonisation from upstream populations. Thus, while resistance and resilience traits can explain species persistence to extreme disturbance, population genomic change varies among species, challenging generalisations about evolutionary responses to extreme events at landscape scales.

Urban expansion has widespread impacts on wildlife species globally, including the transmission and emergence of infectious diseases. However, there is almost no information about how urban landscapes shape transmission dynamics in wildlife. Using an innovative phylodynamic approach combining host and pathogen molecular data with landscape characteristics and host traits, we untangle the complex factors that drive transmission networks of feline immunodeficiency virus (FIV) in bobcats (Lynx rufus). We found that the urban landscape played a significant role in shaping FIV transmission. Even though bobcats were often trapped within the urban matrix, FIV transmission events were more likely to occur in areas with more natural habitat elements. Urban fragmentation also resulted in lower rates of pathogen evolution, possibly owing to a narrower range of host genotypes in the fragmented area. Combined, our findings show that urban landscapes can have impacts on a pathogen and its evolution in a carnivore living in one of the most fragmented and urban systems in North America. The analytical approach used here can be broadly applied to other host–pathogen systems, including humans.

Human land use is fragmenting habitats worldwide and inhibiting dispersal among previously connected populations of organisms, often leading to inbreeding depression and reduced evolutionary potential in the face of rapid environmental change. To combat this augmentation of isolated populations with immigrants is sometimes used to facilitate demographic and genetic rescue. Augmentation with immigrants that are genetically and adaptively similar to the target population effectively increases population fitness, but if immigrants are very genetically or adaptively divergent, augmentation can lead to outbreeding depression. Despite well-cited guidelines for the best practice selection of immigrant sources, often only highly divergent populations remain, and experimental tests of these riskier augmentation scenarios are essentially nonexistent. We conducted a mesocosm experiment with Trinidadian guppies (Poecilia reticulata) to test the multigenerational demographic and genetic effects of augmenting 2 target populations with 3 types of divergent immigrants. We found no evidence of demographic rescue, but we did observe genetic rescue in one population. Divergent immigrant treatments tended to maintain greater genetic diversity, abundance, and hybrid fitness than controls that received immigrants from the source used to seed the mesocosms. In the second population, divergent immigrants had a slightly negative effect in one treatment, and the benefits of augmentation were less apparent overall, likely because this population started with higher genetic diversity and a lower reproductive rate that limited genetic admixture. Our results add to a growing consensus that gene flow can increase population fitness even when immigrants are more highly divergent and may help reduce uncertainty about the use of augmentation in conservation.

In eastern East Africa (the southern Ethiopia, eastern Kenya and southern Somalia region), poor boreal spring (long wet season) rains in 1999, 2000, 2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers implement disaster risk reduction measures while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent East African droughts to a stronger Walker circulation, resulting from warming in the Indo–Pacific warm pool and an increased east-to-west sea surface temperature (SST) gradient in the western Pacific, we show that the two dominant modes of East African boreal spring rainfall variability are tied to SST fluctuations in the western central Pacific and central Indian Ocean, respectively. Variations in these two rainfall modes can thus be predicted using two SST indices – the western Pacific gradient (WPG) and central Indian Ocean index (CIO), with our statistical forecasts exhibiting reasonable cross-validated skill (rcv ≈ 0.6). In contrast, the current generation of coupled forecast models show no skill during the long rains. Our SST indices also appear to capture most of the major recent drought events such as 2000, 2009 and 2011. Predictions based on these simple indices can be used to support regional forecasting efforts and land surface data assimilations to help inform early warning and guide climate outlooks.

Mainstreaming disaster risk reduction is a novel perspective of managing natural hazards. Risk analysis consists of four basic analytical modules – hazard, exposure, vulnerability and risk to determine risk metrics. Risk metrics are losses corresponding to different return periods and their likelihood derived from stochastic evaluations of historical and simulated loss data – this process is called risk profiling for a given natural hazard in a given region. The U.S. Agency for International Development (USAID) funded Famine Early Warning Systems Network (FEWS NET) has been monitoring food security issues in the sub-Saharan Africa, Asia, and Central America and in Haiti. FEWS NET uses satellite based rainfall and climatic water demands to monitor moisture availability conditions for deriving food security status in Africa. This paper highlights the results of agricultural drought risk profiling analyses for maize in Malawi and Mozambique in Southern Africa, and for millet in Niger in West Sahel. Historical maize and millet yields have been analyzed to develop drought vulnerability models using the satellite rainfall based-water requirement satisfaction index (WRSI) in the above countries. In view of the limited hazard and exposure data (2000 to present) long-term synthetic rainfall time series were generated. The loss exceedance probability (LEP) curves, return period losses and drought frequency maps indicating district-level drought-proneness for the target crops in the selected countries have been generated.

A wide range of sea surface temperature (SST) expressions have been observed during the El Nino–Southern Oscillation events of 1950–2010, which have occurred simultaneously with different global atmospheric circulations. This study examines the atmospheric circulation and precipitation during December–March 1950–2010 over the African Continent south of 15 $$^{\circ }$$ S, a region hereafter known as Southern Africa, associated with eight tropical Pacific SST expressions characteristic of El Nino and La Nina events. The self-organizing map method along with a statistical distinguishability test was used to isolate the SST expressions of El Nino and La Nina. The seasonal precipitation forcing over Southern Africa associated with the eight SST expressions was investigated in terms of the horizontal winds, moisture budget and vertical motion. El Nino events, with warm SST across the east and central Pacific Ocean and warmer than average SST over the Indian Ocean, are associated with precipitation reductions over Southern Africa. The regional precipitation reductions are forced primarily by large-scale mid-tropospheric subsidence associated with anticyclonic circulation in the upper troposphere. El Nino events with cooler than average SST over the Indian Ocean are associated with precipitation increases over Southern Africa associated with lower tropospheric cyclonic circulation and mid-tropospheric ascent. La Nina events, with cool SST anomalies over the central Pacific and warm SST over the west Pacific and Indian Ocean, are associated with precipitation increases over Southern Africa. The regional precipitation increases are forced primarily by lower tropospheric cyclonic circulation, resulting in mid-tropospheric ascent and an increased flux of moisture into the region.

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this forecast system for a region of equatorial EA (2° S–8° N, 36–46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM forecasts generated by this system on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM forecasts with start-of-season (SOS) (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the forecast was initialized with midseason (i.e., 5 April) SM conditions, the skill of forecasting SM estimates until the end-of-season improved (correlation > 0.5 over several grid cells). We also found these SM forecasts to be more skillful than the ones generated using the Ensemble Streamflow Prediction (ESP) method, which derives its hydrologic forecast skill solely from the knowledge of the initial hydrologic conditions. Finally, we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years (when standardized anomaly of MAM precipitation is below 0). This indicates that this system might be particularity useful for identifying drought events in this region and can support decision-making for mitigation or humanitarian assistance.

Biodiversity loss is a global phenomenon that can result in the collapse of food webs and critical ecosystem services. Amphibian population decline over the last century is a notable case of species loss because amphibians survived the last four major extinction events in global history, their current rate of extinction is unprecedented, and their rate of extinction is greater than that for most other taxonomic groups. Despite the severity of this conservation problem and its relevance to the study of global biodiversity loss, major knowledge gaps remain for many of the most threatened species and regions in the world. Rigorous estimates of population parameters are lacking for many amphibian species in the Neotropics. The goal of our study was to determine how the demography of seven species of the genus Pristimantis varied over time and space in two cloud forests in the Ecuadorian Andes. We completed a long term capture–mark–recapture study to estimate abundance, survival, and population growth rates in two cloud forests in the Ecuadorian Andes; from 2002 to 2009 at Yanayacu in the Eastern Cordillera and from 2002 to 2003 at Cashca Totoras in the Western Cordillera. Our results showed seasonal and annual variation in population parameters by species and sex. P. bicantus experienced significant reductions in abundance over the course of our study. Abundance, apparent survival, and population growth rates were lower in disturbed than in primary or mature secondary forest. The results of our study raise concerns for the population status of understudied amphibian groups and provide insights into the population dynamics of Neotropical amphibians.

Anthropogenic threat maps are commonly used as a surrogate for the ecological integrity of rivers in freshwater conservation, but a clearer understanding of their relationships is required to develop proper management plans at large scales. Here, we developed and validated empirical models that link the ecological integrity of rivers to threat maps in a large, heterogeneous and biodiverse Andean–Amazon watershed. Through fieldwork, we recorded data on aquatic invertebrate community composition, habitat quality, and physical-chemical parameters to calculate the ecological integrity of 140 streams/rivers across the basin. Simultaneously, we generated maps that describe the location, extent, and magnitude of impact of nine anthropogenic threats to freshwater systems in the basin. Through seven-fold cross-validation procedure, we found that regression models based on anthropogenic threats alone have limited power for predicting the ecological integrity of rivers. However, the prediction accuracy improved when environmental predictors (slope and elevation) were included, and more so when the predictions were carried out at a coarser scale, such as microbasins. Moreover, anthropogenic threats that amplify the incidence of other pressures (roads, human settlements and oil activities) are the most relevant predictors of ecological integrity. We concluded that threat maps can offer an overall picture of the ecological integrity pattern of the basin, becoming a useful tool for broad-scale conservation planning for freshwater ecosystems. While it is always advisable to have finer scale in situ measurements of ecological integrity, our study shows that threat maps provide fast and cost-effective results, which so often are needed for pressing management and conservation actions.

The differences in tropical Pacific sea surface temperature (SST) expressions of El Nino-Southern Oscillation (ENSO) events of the same phase have been linked with different global atmospheric circulation patterns. This study examines the dynamical forcing of precipitation during October–December (OND) and March–May (MAM) over East Africa and during December–March (DJFM) over Central-Southwest Asia for 1950–2010 associated with four tropical Pacific SST patterns characteristic of La Nina events, the cold phase of ENSO. The self-organizing map method along with a statistical distinguishability test was used to isolate La Nina events, and seasonal precipitation forcing was investigated in terms of the tropical overturning circulation and thermodynamic and moisture budgets. Recent La Nina events with strong opposing SST anomalies between the central and western Pacific Ocean (phases 3 and 4), force the strongest global circulation modifications and drought over the Northwest Indian Ocean Rim. Over East Africa during MAM and OND, subsidence is forced by an enhanced tropical overturning circulation and precipitation reductions are exacerbated by increases in moisture flux divergence. Over Central-Southwest Asia during DJFM, the thermodynamic forcing of subsidence is primarily responsible for precipitation reductions, with moisture flux divergence acting as a secondary mechanism to reduce precipitation. Eastern Pacific La Nina events in the absence of west Pacific SST anomalies (phases 1 and 2), are associated with weaker global teleconnections, particularly over the Indian Ocean Rim. The weak regional teleconnections result in statistically insignificant precipitation modifications over East Africa and Central-Southwest Asia.

We present an integrative measure of exposure and sensitivity components of vulnerability to climatic and demographic change for the African continent in order to identify “hot spots” of high potential population vulnerability. Getis-Ord Gi* spatial clustering analyses reveal statistically significant locations of spatio-temporal precipitation decline coinciding with high population density and increase. Statistically significant areas are evident, particularly across central, southern, and eastern Africa. The highly populated Lake Victoria basin emerges as a particularly salient hot spot. People located in the regions highlighted in this analysis suffer exceptionally high exposure to negative climate change impacts (as populations increase on lands with decreasing rainfall). Results may help inform further hot spot mapping and related research on demographic vulnerabilities to climate change. Results may also inform more suitable geographical targeting of policy interventions across the continent.

A previous study analyzed errors in the numerical calculation of actual crop evapotranspiration (ETa) under soil water stress. Assuming no irrigation or precipitation, it constructed equations for ETa over limited soil-water ranges in a root zone drying out due to evapotranspiration. It then used a single crop-soil composite to provide recommendations about the appropriate usage of numerical methods under different values of the time step and the maximum crop evapotranspiration (ETc). This comment reformulates those ETa equations for applicability over the full range of soil water values, revealing a dependence of the relative error in numerical ETa on the initial soil water that was not seen in the previous study. It is shown that the recommendations based on a single crop-soil composite can be invalid for other crop-soil composites. Finally, a consideration of the numerical error in the time-cumulative value of ETa is discussed besides the existing consideration of that error over individual time steps as done in the previous study. This cumulative ETa is more relevant to the final crop yield. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

The El Nino–Southern Oscillation (ENSO) is the leading mode of interannual variability, with global impacts on weather and climate that have seasonal predictability. Research on the link between interannual ENSO variability and the leading mode of intraseasonal variability, the Madden–Julian oscillation (MJO), has focused mainly on the role of MJO initiating or terminating ENSO. We use observational analysis and modeling to show that the MJO has an important simultaneous link to ENSO: strong MJO activity significantly weakens the atmospheric branch of ENSO. For weak MJO conditions relative to strong MJO conditions, the average magnitude of ENSO-associated tropical precipitation anomalies increases by 63%, and the strength of hemispheric teleconnections increases by 58%. Since the MJO has predictability beyond three weeks, the relationships shown here suggest that there may be subseasonal predictability of the ENSO teleconnections to continental circulation and precipitation.

Most invasive alien vertebrate populations on the Channel Islands of California have been eradicated over the past 30 years. Unfortunately, removal of these introduced herbivores or predators came too late for some native flora and fauna, and numerous populations are now extinct. Here, we describe a systematic approach to reintroducing extirpated native taxa as a means for rebuilding natural communities and enhancing the resiliency of island ecosystems. Reintroduction efforts typically focus on a single species or site. In contrast, we propose that if reintroduction is a shared conservation goal of managers across the islands, the associated planning, implementation, and monitoring should be conducted as a cross-island initiative for the archipelago. A coordinated effort based on best practices in reintroduction biology could accrue programmatic efficiencies and economies of scale, more quickly advance ecosystem and species conservation goals, and create unique opportunities to test hypotheses in basic and applied ecology and evolution. The philosophical and technical approaches developed through this program may apply to other island and mainland sys- tems and could be adapted to develop conservation strategies for species that may be candidates for assisted coloniza- tion in the face of climate change. RESUMEN.—La mayoria de las poblaciones invasoras de vertebrados no nativas en las Islas del Canal de California han sido erradicadas durante los ultimos 30 anos. Desafortunadamente, la eliminacion de estos herbivoros o depredadores exoticos llego demasiado tarde para algunas especies de fauna y flora nativa, y numerosas poblaciones estan ahora extin- tas. Describimos un enfoque sistematico para reintroducir taxa autoctonos eliminados como un medio de reconstruir las comunidades naturales y mejorar la resistencia de los ecosistemas de las islas. Los esfuerzos de reintroduccion tipica- mente se centran en una sola especie o lugar. Por el contrario, nosotros proponemos que si la reintroduccion es un obje- tivo de conservacion compartido por los administradores de recursos en todas las islas, la planificacion asociada, la implementacion y el monitoreo deben ser conducidos como una iniciativa entre islas del archipielago. Un esfuerzo coor- dinado, basado en mejores practicas de biologia de reintroduccion, podria conjuntar eficiencia del programa y economias de escala, hacer que los objetivos de conservacion de los ecosistemas avancen mas rapidamente y crear opor- tunidades unicas de comprobar hipotesis sobre ecologia y evolucion basica y aplicada. Los enfoques filosoficos y tecni- cos desarrollados a traves de este programa podrian aplicarse a otros sistemas de islas y de continentes, y podrian ser adaptados para desarrollar estrategias de conservacion para aquellas especies que pudieran ser candidatas para la colo- nizacion asistida en caso de cambio climatico.

Islands host exceptionally high levels of endemism compared to mainland regions and are subject to disproportionately high rates of extinction and imperilment. Therefore, the protection and preservation of taxonomic units that are endemic to islands is a key component in mitigating the loss of global biodiversity. However, determining what is “endemic” on islands can be challenging. Conservation units are commonly delineated based on genetic divergence at neutral loci (e.g., genetic differentiation at microsatellite loci or reciprocal monophyly based on mitochondrial genes). Island populations of nonvolant species are expected to meet this criterion, regardless of adaptive differences, due to geographic isolation, founder effects, and small effective population sizes. We therefore argue that the delineation and management of island endemic populations should not be based on neutral genetic divergence and reciprocal monophyly alone. Instead, we recommend identifying island populations that have genetically based adaptations to their unique environments. A comprehensive framework specifically designed to delineate evolutionarily significant units (ESUs) on islands should be based on metrics of both neutral and adaptive genetic divergence. The California Channel Islands host several taxa considered to be endemic, and we highlight 2 case studies to illustrate how this framework can be applied. This approach can be applied broadly to continental islands and island archipelagos, enabling conservation practitioners to use an objective framework to prioritize units of biological diversity for management.

result little is known about this life-history phase for most amphibians. Our focus was to explore survival probabilities, mark retention, and mark migration in postmetamorphic Boreal Chorus Frogs (Psuedacris maculata) in a laboratory setting. One hundred forty-seven individuals were assigned randomly to two treatment groups or a control group. Frogs in the first treatment group were marked with visual implant elastomer, while frogs in the second treatment group were toe clipped. Growth and mortality were recorded for one year and resulting data were analyzed using known-fate models in Program MARK. Model selection results suggested that survival probabilities of frogs varied with time and showed some variation among marking treatments. We found that frogs with multiple toes clipped on the same foot had lower survival probabilities than individuals in other treatments, but individuals can be marked by clipping a single toe on two different feet without any mark loss or negative survival effects. Individuals treated with visual implant elastomer had a mark migration rate of 4% and mark loss rate of 6%, and also showed very little negative survival impacts relative to control individuals.

El Niño–Southern Oscillation (ENSO) events are accompanied by an anomalous zonal sea surface temperature (SST) gradient over the west Pacific Ocean, defined here as the west Pacific SST gradient (WPG). The WPG is defined as the standardized difference between area-averaged SST over the central Pacific Ocean (Niño-4 region) and west Pacific Ocean (0°–10°N, 130°–150°E). While the direction of the WPG follows ENSO cycles, the magnitude of the gradient varies considerably between individual El Niño and La Niña events. In this study, El Niño and La Niña events are grouped according to the magnitude of the WPG, and tropical SST, circulations, and precipitation are examined for the period 1948–2011. Until the 1980s the WPG showed little trend as the west and central Pacific warmed at similar rates; however, the west Pacific has recently warmed faster than the central Pacific, which has resulted in an increased WPG during La Niña events. The temporal evolution and distribution of tropical Pacific SST as well as the near-surface tropical Pacific zonal wind, divergence, and vertical velocity are considerably different during ENSO events partitioned according to the strength of the WPG. Modifications to the tropical circulation, resulting in changes to Indo– west Pacific precipitation, are linked to strong and consistent circulation and precipitation modifications throughout the Northern Hemisphere during winter.

Carbon dioxide is emitted from the combustion of fossil fuels and is an important contributor to anthropogenic climate change. Multiple current and planned satellite missions are designed to quantify atmospheric carbon dioxide concentrations on a global scale, but most of these sensors do not have the spatial resolution necessary to resolve point sources such as fossil fuel power plants. Airborne imaging spectrometer data, such as those from the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), can have multiple, contiguous bands covering shortwave infrared (SWIR) absorption features produced by carbon dioxide. Therefore, high spatial resolution data from AVIRIS-like sensors may offer a means for detecting plumes and retrieving carbon dioxide concentrations for point source emissions. The objectives of this study include modeling minimum carbon dioxide anomalies detectable in AVIRIS data under different conditions and applying a Cluster-Tuned Matched Filter for detection of carbon dioxide plumes in simulated data and in AVIRIS images acquired over power plants. Radiative transfer simulations were used to model the residual radiance produced by increased absorption by carbon dioxide as concentration was elevated above background levels within a 0–500 m layer. Carbon dioxide anomalies, surface reflectance, water vapor concentration, solar zenith angle, sensor height, and aerosol scattering were varied in simulation sets and the resulting residual radiance spectra were compared against noise equivalent delta radiance (NEdL) for the “classic” and “next generation” AVIRIS instruments. Sensitivity to carbon dioxide anomalies improved with increased surface reflectance and declined with increased water vapor concentration, solar zenith angle, sensor height, and aerosol scattering. Zero to 500 m concentration anomalies as low as 100 parts per milion by volume (ppm) for AVIRIS C and 25 ppm for AVIRIS NG produced residual radiance values that exceeded SWIR NEdL. Carbon dioxide concentrations modeled for a generic power plant emissions scenario using a plume dispersion model were combined with randomly-generated reflectance spectra to create simulated images with varying surface reflectance and NEdL. For these simulated images, true positive and false positive detection rates improved as background reflectance increased and as NEdL decreased. Apparent plumes were detected in all four AVIRIS C images acquired over power plants, although the characteristics of the plumes varied according to solar-plume-sensor geometry. Improvements in modeling may allow retrieval of plume concentration, providing a valuable means for quantifying point source emissions and a basis for comparison with column concentrations retrieved from in situ measurements and coarse resolution satellite data.

Increasing climate variability and extreme weather conditions along with declining trends in both rainfall and temperature represent major risk factors affecting agricultural production and food security in many regions of the world. The rangelands of Ethiopia, Kenya, and Somalia in the East African Horn remain one of the world's most food insecure regions, yet have substantially increasing human populations predominantly dependent on pastoralist and agro-pastoralist livelihoods. We identify regions where substantial rainfall decrease between two periods interrupted by the 1998 El Nino event (1981–2012) in the East African Horn is coupled with human population density increases. Vegetation in this region is characterized by a variable mosaic of land covers, generally dominated by grasslands necessary for agro-pastoralism, interspersed by woody vegetation. Recent assessments indicate that vegetation degradation is occurring, adversely impacting fragile ecosystems and human livelihoods. Using AVHRR and MODIS vegetation products from 1981 to 2012, we observe changes in vegetation patterns and productivity over the last decade across the East African Horn. We observe vegetation browning trends in areas experiencing reduced main-growing season precipitation; these areas are also concurrently experiencing increasing population pressures. We also found that the drying precipitation patterns only partially statistically explain the vegetation browning trends, indicating that other factors such as population pressures and land use changes might be responsible for the observed declining vegetation condition. Furthermore, we show that the general vegetation browning trends persist even during years with normal rainfall conditions such as 2012, pointing to potential long-term degradation of rangelands on which approximately 10 million people depend. These findings may have implications for current and future regional food security monitoring and forecasting as well as for mitigation and adaptation strategies in a region where population is expected to continue increasing against a backdrop of drying climate trends and increased climatic variability.

Rainfall over eastern Africa (10°S–10°N; 35°E–50°E) is bimodal, with seasonal maxima during the "long rains" of March–April–May (MAM) and the "short rains" of October–November–December (OND). Below average precipitation during consecutive long and short rains seasons over eastern Africa can have devastating long-term impacts on water availability and agriculture. Here, we examine the forcing of drought during consecutive long and short rains seasons over eastern Africa by Indo-Pacific sea surface temperatures (SSTs). The forcing of eastern Africa precipitation and circulation by SSTs is tested using ten ensemble simulations of a global weather forecast model forced by 1950–2010 observed global SSTs. Since the 1980s, Indo-Pacific SSTs have forced more frequent droughts spanning consecutive long and short rains seasons over eastern Africa. The increased frequency of dry conditions is linked to warming SSTs over the Indo-west Pacific and to a lesser degree to Pacific Decadal Variability. During MAM, long-term warming of tropical west Pacific SSTs from 1950–2010 has forced statistically significant precipitation reductions over eastern Africa. The warming west Pacific SSTs have forced changes in the regional lower tropospheric circulation by weakening the Somali Jet, which has reduced moisture and rainfall over the Horn of Africa. During OND, reductions in precipitation over recent decades are oftentimes overshadowed by strong year-to-year precipitation variability forced by the Indian Ocean Dipole and the El Nino–Southern Oscillation.

In this study, a Cluster-Tuned Matched Filter (CTMF) technique was applied to data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over marine and terrestrial locations known to emit methane (CH4). At the Coal Oil Point marine seep field, prominent CH4 anomalies were consistent with advection from known areas of active seepage. For a region with natural CH4 and oil seepage located west of downtown Los Angeles, significant CH4 anomalies were identified for known sources at the La Brea Tar Pits and in close proximity to probable sources, including an office complex documented as venting CH4 continuously and hydrocarbon storage tanks on the Inglewood Oil Field. However, interpretation of anomalies was complicated by noise and false positives for surfaces with strong absorptions at the same wavelengths as CH4 absorption features. Segmentation of results identified 16 distinct locations of contiguous pixels with high CTMF scores and segments were classified into probable CH4 anomalies and confusers based on the spectral properties of the underlying surface over the full spectral range measured by AVIRIS. This technique is particularly well suited for application over large areas to detect CH4 emissions from concentrated point sources and should permit detection of additional trace gasses with distinct absorption features, including carbon dioxide (CO2) and nitrous oxide (N2O). Thus, imaging spectrometry by an AVIRIS-like sensor has the potential to improve high resolution greenhouse gas mapping, better constraining local sources.

During 1990s, disaster risk reduction emerged as a novel, proactive approach to managing risks from natural hazards. The World Bank, USAID, and other international donor agencies began making efforts to mainstream disaster risk reduction in countries whose population and economies were heavily dependent on rain-fed agriculture. This approach has more significance in light of the increasing climatic hazard patterns and the climate scenarios projected for different hazard prone countries in the world. The Famine Early Warning System Network (FEWS NET) has been monitoring the food security issues in the sub-Saharan Africa, Asia and in Haiti. FEWS NET monitors the rainfall and moisture availability conditions with the help of NOAA RFE2 data for deriving food security status in Africa. This paper highlights the efforts in using satellite estimated rainfall inputs to develop drought vulnerability models in the drought prone areas in Malawi. The satellite RFE2 based SPI corresponding to the critical tasseling and silking phases (in the months of January, February, and March) were statistically regressed with drought-induced yield losses at the district level. The analysis has shown that the drought conditions in February and early March lead to most damage to maize yields in this region. The district-wise vulnerabilities to drought were upscaled to obtain a regional maize vulnerability model for southern Malawi. The results would help in establishing an early monitoring mechanism for drought impact assessment, give the decision makers additional time to assess seasonal outcomes, and identify potential food-related hazards in Malawi.

We used an occupancy modeling framework to test the relative importance of upland habitat composition (proportion of different types of vegetation and development) and pond characteristics (water chemistry and vegetation) in determining the occurrence of two species of pond-breeding salamanders, the Marbled Salamander (Ambystoma opacum) and Mabee's Salamander (Ambystoma mabeei), in an urban nature reserve in southeastern Virginia, USA. Of 55 ponds surveyed in 2009, we found A. opacum larvae in 17 (30.9%) ponds and A. mabeei larvae in seven (12.7%) ponds. We found a strong positive relationship between A. opacum larval occupancy probability and pH; salamanders were more likely to occupy less acidic waters. Sampled ponds were highly acidic, with pH values ranging between 3.36 and 4.41, but A. opacum occupied only ponds with pH values >3.66. Ambystoma mabeei was more tolerant of the highly acidic conditions and was more likely to occupy larger ponds with fewer trees and scrubs. Landscape-level model...

Northern Hemisphere circulations differ considerably between individual El Nino-Southern Oscillation events due to internal atmospheric variability and variation in the zonal location of sea surface temperature forcing over the tropical Pacific Ocean. This study examines the similarities between recent Northern Hemisphere droughts associated with La Nina events and anomalously warm tropical west Pacific sea surface temperatures during 1988–1989, 1998–2000, 2007–2008 and 2010–2011 in terms of the hemispheric-scale circulations and the regional forcing of precipitation over North America and Asia during the cold season of November through April. The continental precipitation reductions associated with recent central Pacific La Nina events were most severe over North America, eastern Africa, the Middle East and southwest Asia. High pressure dominated the entire Northern Hemisphere mid-latitudes and weakened and displaced storm tracks northward over North America into central Canada. Regionally over North America and Asia, the position of anomalous circulations within the zonal band of mid-latitude high pressure varied between each La Nina event. Over the northwestern and southeastern United States and southern Asia, the interactions of anomalous circulations resulted in consistent regional temperature advection, which was subsequently balanced by similar precipitation-modifying vertical motions. Over the central and northeastern United States, the spatial variation of anomalous circulations resulted in modest inter-seasonal temperature advection variations, which were balanced by varying vertical motion and precipitation patterns. Over the Middle East and eastern Africa, the divergence of moisture and the advection of dry air due to anomalous circulations enhanced each of the droughts.

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa. Machine-accessible metadata file describing the reported data (ISA-Tab format)

The 1981–2014 climatology and variability of the March–May eastern Horn of Africa boreal spring wet season are examined using precipitation, upper- and lower-level winds, low-level specific humidity, and convective available potential energy (CAPE), with the aim of better understanding the establishment of the wet season and the cause of the recent observed decline. At 850 mb, the development of the wet season is characterized by increasing specific humidity and winds that veer from northeasterly in February to southerly in June and advect moisture into the region, in agreement with an earlier study. Equally important, however, is a substantial weakening of the 200-mb climatological easterly winds in March. Likewise, the shutdown of the wet season coincides with the return of strong easterly winds in June. Similar changes are seen in the daily evolution of specific humidity and 200-mb wind when composited relative to the interannual wet season onset and end, with the easterlies decreasing (increasing) several days prior to the start (end) of the wet season. The 1981–2014 decrease in March–May precipitation has also coincided with an increase in 200-mb easterly winds, with no attendant change in specific humidity, leading to the conclusion that, while high values of specific humidity are an important ingredient of the wet season, the recent observed precipitation decline has resulted mostly from a strengthening of the 200-mb easterlies. This change in the easterly winds appears to be related to an increase in convection over the Indonesian region and in the associated outflow from that enhanced heat source.

Geographic barriers and elevational gradients have long been recognized as important in species diversification. Here, we illustrate an example where both mechanisms have shaped the genetic structure of the Neotropical rainfrog, Pristimantis ornatissimus, which has also resulted in speciation. This species was thought to be a single evolutionary lineage distributed throughout the Ecuadorian Choco and the adjacent foothills of the Andes. Based on recent sampling of P. ornatissimus sensu lato, we provide molecular and morphological evidence that support the validity of a new species, which we name Pristimantis ecuadorensis sp. nov. The sister species are elevational replacements of each other; the distribution of Pristimantis ornatissimus sensu stricto is limited to the Ecuadorian Choco ecoregion (< 1100 m), whereas the new species has only been found at Andean localities between 1450–1480 m. Given the results of the Multiple Matrix Regression with Randomization analysis, the genetic difference between P. ecuadorensis and P. ornatissimus is not explained by geographic distance nor environment, although environmental variables at a finer scale need to be tested. Therefore this speciation event might be the byproduct of stochastic historic extinction of connected populations or biogeographic events caused by barriers to dispersal such as rivers. Within P. ornatissimus sensu stricto, morphological patterns and genetic structure seem to be related to geographic isolation (e.g., rivers). Finally, we provide an updated phylogeny for the genus, including the new species, as well as other Ecuadorian Pristimantis.

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

Habitat suitability is strongly regulated by seasonal conditions and stochastic processes, and this is especially important in temporary aquatic systems that contain organisms with complex life cycles. We investigated the potential for phenotypic plasticity in timing of and size at metamorphosis to mitigate effects of altered habitat conditions, specifically shortened hydroperiod (duration of water in ponds) and altered predator‐prey dynamics, in the pond‐breeding boreal chorus frog (Pseudacris maculata). We simulated reduced hydroperiod and concentrated predator cue in the laboratory to understand potential benefits and costs of plasticity. Tadpoles developed faster in response to the combined effects of reduced hydroperiod and increased concentration of predator cue, potentially due to reduced conspecific density. In contrast, there was no effect of reduced hydroperiod or predator cue on size at metamorphosis. Alone, this result suggests that phenotypic plasticity may allow P. maculata to escape the negative effects of rapidly drying ponds. However, tadpole survival was significantly lower in reduced hydroperiod treatments relative to all other treatments, suggesting that even if plasticity acts as a buffer against reduced hydroperiod by facilitating metamorphosis, heightened mortality may offset benefits of this rapid response. Our results add to previous studies of plastic responses in amphibians by disentangling the costs and benefits of plasticity in habitats with multiple, simultaneous stressors. We show that while plasticity may accelerate metamorphosis, similar, heightened levels of mortality are experienced regardless of plasticity. This implies that plasticity may not completely buffer populations against the effects of altered habitat conditions, such as those that occur with climate change or urbanization.

Despite growing research into the socio-economic aspects of vulnerability [1] – [4] , relatively little work has linked population dynamics with climate change beyond the complex relationship between migration and climate change [5] . It is likely, however, that most people experience climate change in situ , so understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [6] . This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [7] , we model child undernutrition vulnerability indices based on climate variables, including proxy measures (NDVI) derived from satellite imagery, at a 5-km spatial resolution. Results suggest that vegetation changes associated with precipitation decline in rural areas of sub-Saharan Africa can help predict deteriorating child health.

Understanding the relationship between climate-driven habitat conditions and survival is key to preserving biodiversity in the face of rapid climate change. Hydroperiod—the length of time water is in a wetland—is a critical limiting habitat variable for amphibians as larvae must metamorphose before ponds dry. Changes in precipitation and temperature patterns are affecting hydroperiod globally, but the impact of these changes on amphibian persistence is poorly understood. We studied the responses of Boreal Chorus Frog (Pseudacris maculata) tadpoles to simulated hydroperiods (i.e., water level reductions) in the laboratory using individuals collected from ponds spanning a range of natural hydroperiods (Colorado Front Range, USA). To assess the effects of experimental hydroperiod reduction, we measured mortality, time to metamorphosis, and size at metamorphosis. We found that tadpoles grew at rates reflecting the hydroperiods of their native ponds, regardless of experimental treatment. Tadpoles from permanent ponds metamorphosed faster than those from ephemeral ponds across all experimental treatments, a pattern which may represent a predation selection gradient or countergradient variation in developmental rates. Size at metamorphosis did not vary across experimental treatments. Mortality was low overall but varied with pond of origin. Our results suggest that adaptation to local hydroperiod and/or predation and temperature conditions is important in P. maculata. Moreover, the lack of a plastic response to reduced hydroperiods suggests that P. maculata may not be able to metamorphose quickly enough to escape drying ponds. These results have important implications for amphibian persistence in ponds predicted to dry more quickly due to rapid climate change.

In Kenya – where undernutrition rates are high and the population-environment balance is delicate – the risk of declining food availability has become increasingly concerning as indications of drying trends threaten current food systems. The purpose of this research is to determine if climate variables are related to rates of childhood stunting in Kenya. Specifically we use multi-level regression models at the cluster/household level to evaluate the correlation between surface temperatures, rainfall levels and stunting among children aged one to five. Our results suggest that as Kenya continues to experience warming and drying, malnutrition rates will increase. We propose that investments in infrastructure and expansion of education can mitigate the negative impacts of climate change.

Genomic data have the potential to revolutionize the delineation of conservation units (CUs) by allowing the detection of adaptive genetic variation, which is otherwise difficult for rare, endangered species. In contrast to previous recommendations, we propose that the use of neutral versus adaptive markers should not be viewed as alternatives. Rather, neutral and adaptive markers provide different types of information that should be combined to make optimal management decisions. Genetic patterns at neutral markers reflect the interaction of gene flow and genetic drift that affects genome-wide variation within and among populations. This population genetic structure is what natural selection operates on to cause adaptive divergence. Here, we provide a new framework to integrate data on neutral and adaptive markers to protect biodiversity.