Spatial Climate Variability Research Articles

Spatial and temporal variability in Mediterranean climate over the last millennium from vermetid isotope records and CMIP5/PMIP3 models

Abstract Stable isotope compositions of oxygen (δ18O) and carbon (δ13C) of the aragonite skeleton of the reef-building gastropod Dendropoma petreaum provide high-resolution records of the Mediterranean climate over the last millennium. In particular, the isotopic composition of vermetid cores collected from the west and east Mediterranean reveals that the different regions have had distinct thermal and primary production behaviors throughout the last millennium. The rate of warming in the recent Industrial Period is variable among the different regions of the Mediterranean Sea. The δ18O-derived Sea Surface Temperature (SST) anomalies show that the Eastern Mediterranean surface temperature is increasing much more rapidly than in the Western Mediterranean. Additionally, the signals of the Little Ice Age and of the Medieval Climate Anomaly are more apparent in the western and in the central Mediterranean while they are almost absent in the eastern Mediterranean. We aim to reconcile the SST temporal and spatial pattern with the variability of the North Atlantic Oscillation (NAO) and the South Asian Monsoon (SAM) climate systems by analyzing coupled atmosphere-ocean models from the CMIP5/PMIP3 projects that simulate the global climate of the past 1000 years. We show that even though the NAO is more dominant in the western Mediterranean SST, its latitudinal movement, on a centennial time scale, is evident in the eastern Mediterranean SST signal. We also discuss the Atlantic water inflow role in the observed similarities of the surface productivity signals and the Suess effect that prevail the Industrial Period signal in all regions of the Mediterranean.

Environmental Drivers and Distribution Patterns of Carnivoran Assemblages (Mammalia: Carnivora) in the Americas: Past to Present

Understanding species distributions and the variation of assemblage structure in time and space are fundamental goals of biogeography and ecology. Here, we use an ecological niche modeling and macroecological approach in order to assess whether constraints patterns in carnivoran richness and composition structures in replicated assemblages through time and space should reflect environmental filtering through ecological niche constraints from the Last Inter-glacial (LIG), Last Glacial Maximum (LGM) to the present (C) time. Our results suggest a diverse distribution of carnivoran co-occurrence patterns at the continental scale as a result of spatial climatic variation as an important driver constrained by the ecological niches of the species. This influence was an important factor restructuring assemblages (more directly on richness than composition patterns) not only at the continental level, but also from regional and local scales and this influence was geographically different throughout the space in the continent. These climatic restrictions and disruption of the niche during the environmental changes at the LIG-LGM-C transition show a considerable shift in assemblage richness and composition across the Americas, which suggests an environmental filtering mainly during the LGM, explaining between 30 and 75% of these variations through space and time, with more accentuated changes in North than South America. LGM was likely to be critical in species functional adaptation and distribution and therefore on assemblage structuring and rearranging from continental to local scales through time in the continent. Still, extinction processes are the result of many interacting factors, where climate is just one part of the picture.

A classification system for global wave energy resources based on multivariate clustering

Better understanding of the global wave climate is required to inform wave energy device design and large-scale deployment. Spatial variability in the global wave climate is analysed here to provide a range of characteristic design wave climates. K-means clustering was used to split the global wave resource into 6 classes in a device agnostic, data-driven method using data from the ECMWF ERA5 reanalysis product. Classification using two sets of input data were considered: a simple set (based on significant wave height and peak wave period) and a comprehensive set including a wide range of relevant wave climate parameters. Both classifications gave resource classes with similar characteristics; 55% of tested locations were assigned to the same class. Two classes were low energy, found in enclosed seas and sheltered regions. Two classes were moderate wave energy classes; one swell dominated and the other in areas with wave action often generated by more local storms. Of the two higher energy classes; one was more often found in the northern hemisphere and the other, most energetic, predominantly on the tips of continents in the southern hemisphere. These classes match existing regional understanding of resource. Consideration of publicly available device power matrices showed good performance was primarily realised for the two highest energy resource classes (25–30% of potential deployment locations); it is suggested that effort should focus on optimising devices for additional resource classes. The authors hypothesise that the low-risk, low variability, swell dominated moderate wave energy class would be most suitable for future exploitation.

Spatial variation in climate modifies effects of functional diversity on biomass dynamics in natural forests across Canada

AbstractAimForest net biomass change (ΔAGB; the difference between biomass gain from growth and loss through mortality) determines how forests contribute to the global carbon cycle. Understanding how plant diversity affects ΔAGB in diverse abiotic conditions is crucial in the face of anthropogenic environmental change. Recent studies have advanced our understanding of the effects of plant diversity on growth dependent on the abiotic context, either supporting or rejecting the stress gradient hypothesis. However, we know little about how diversity influences mortality, which prevents us from knowing how diversity affects ΔAGB in diverse abiotic conditions.LocationAcross Canada (43–60° N, 52–133° W).Time period1951–2016.Major taxa studiedNinety‐three tree species.MethodsWe modelled the relationships of growth, mortality and ΔAGB with functional diversity that represented niche complementarity, while simultaneously accounting for the influence of functional identity and stand age.ResultsGrowth and mortality increased, on average, with functional diversity, but the magnitude of the increase in growth was greater than that of mortality, resulting in an increase of ΔAGB. The positive relationship between growth and functional diversity was more prominent in more humid sites than in drier sites. Mortality increased with functional diversity in drier sites but did not increase in wetter sites. The positive relationship between ΔAGB and functional diversity was strengthened with water availability. Moreover, the positive relationship between growth and functional diversity became stronger with temperature, but the positive associations of diversity with mortality and ΔAGB were consistent across the gradient of temperature.Main conclusionsOur results suggest that higher functional diversity leads to an increase in forest biomass accumulation owing to a greater positive effect of functional diversity on productivity than on mortality. However, in contrast to the stress gradient hypothesis, our findings show that the positive effect of functional diversity is more pronounced in an environment favourable for growth.

Spatial and temporal climate variability and change in the bilate catchment, central Rift Valley lakes region, Ethiopia

ABSTRACT The paper explores spatial and temporal variability of rainfall and temperature in Bilate catchment based on monthly data of 11 meteorological stations. Precipitation Concentration Index (PCI) and Pearson product moment correlation were used to estimate monthly concentrations and associations between annual and seasonal rainfall. Similar methods were used to analyze the association of ENSO with rainfall variability using Eastern Equatorial Pacific (Niño 3.4) data, Sea Surface Temperature (SST), and Southern Oscillation Index (SOI). Rainfall and temperature series were tested for trends using the Mann–Kendall test. Rainfall in the catchment is highly seasonal and concentrated in Kiremt (June–September), followed by Belg (February–May). Belg rainfall was highly variable. Annual rainfall was relatively stable in the upper catchment and variable in the lower catchment. PCI values are expected to increase as annual and seasonal rainfall decrease. The middle catchment had significant negative trends in annual and Belg rainfall. November–March SST is strongly associated with Belg rainfall variations throughout the catchment, and ~49% of the variability can be explained by Equatorial Pacific SST. Annual and seasonal temperature significantly increased in middle and lower parts of the catchment in the past 30 years. Warming was ~0.05°C/yr to 0.17°C/yr.

The urban climate of Ghent, Belgium: A case study combining a high-accuracy monitoring network with numerical simulations

As urban environments have a specific climate that poses extra challenges (e.g. increased heat stress during heat waves), gaining detailed insight into the urban climate is important. This paper presents the high-accuracy MOCCA (MOnitoring the City's Climate and Atmosphere) network, which is monitoring the urban climate of the city of Ghent since July 2016. The study illustrates the complementarity between modelling and observing the urban climate. Two different modelling approaches are used: 1 km resolution runs of the SURFEX land surface model and 100 m resolution runs of the computationally cheaper UrbClim boundary layer model. On the one hand, urban models are able to simulate the spatial variability of the urban climate. As such, these models serve as a tool to help deciding on the locations of the measurement stations. On the other hand, the MOCCA observations are used to validate the high-resolution urban model experiments for the summer (July-August-September) of 2016. Our results demonstrate that the models capture the nighttime intra-urban temperature differences, but they are not able to reproduce the observed daytime temperature differences which are determined by the micro-scale environment.

Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks

Abstract. The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

Temporal and spatial variations of urban climate and derivation of an urban climate map for Xi'an, China

Rapid urbanization have increased the sensitivity of urban environment to extreme weather. To improve the deteriorating urban environment, the current situation of urban climate should be evaluated systematically and applied to the planning process. This study reports on the temporal and spatial variability of urban climate and introduces a method for constructing an urban climate map based on spatial statistical analysis of field measurement data. Taking Xi’an, a metropolis in northern China, as the case study, we first conducted a 3-year meteorological investigation, including air temperature, relative humidity, and wind speed in both summer and winter. Meanwhile, five indicators with potential impacts on urban climate were selected to describe the urban characteristics. Then, the geographic information system was applied to integrate the meteorological data and urban characteristic data in order to assign more meteorological data. Next, three types of meteorological single-factor and thermal comfort spatial distribution maps were derived. The k-means clustering analysis method was then employed to classify three types of meteorological factors into seven zones in horizontal spatial dimension. Finally, urban climate maps were generated. This study can help planners quickly distinguish urban climate-sensitive and valuable zones in order to make better strategies for future development.

Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses

AbstractSoil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate‐emissions scenario climate outputs (i.e., RCP4.5–650 ppm CO2 equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short‐term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.

Multiple indices based drought analysis by using long term climatic variables over a part of Koel river basin, India

The present study demonstrates changes in vegetation pattern and climatic variability in past years in the parts of Koel basin in Jharkhand state of India by considering the spatial climatic variability, NDVI anomaly and satellite based drought indices viz Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI). Least square statistical method has been used for assessment of long term climatic fluctuation of major four climatic parameters viz maximum temperature of summer season, minimum temperature of winter season, rainfall of monsoon season, and solar radiation of Rabi and kharif season. Analysis of climate extremes has been done for 26 locations in the study area and then interpolated spatially in Geographical Information System platform. Long term NDVI anomaly shows adverse effect of climate extreme in past 20 years in the study area. The climatic variability exhibits that average maximum temperature during 1979–2017 fluctuates with an increase of 0.50–0.81 °C contrary to a decrease of 0.32–0.15 °C in various parts of study area. Similarly rainfall fluctuates with a decrease of 26–90 mm contrary to an increase of 19–230 mm. Drought prone zones as delineated from spatial overlaying map of VCI, TCI and VHI indicated value from 23 to 55. Major part of the study area severely affected by drought facing water scarcity and mediocre vegetation condition. These areas need proper planning and soil moisture management to overcome the recurrent drought conditions perceived in upcoming years.

Temporal and spatial variability of the open coast wave climate of Victoria, Australia

The open coast of Victoria, Australia, is one of the highest wave energy coastlines globally. Despite this, a lack of permanently deployed wave buoys has limited prior analysis of wave conditions. In this study, the wave climate of Victoria was analysed using 31 years of directional data hindcast from the National Oceanic and Atmospheric Administration’s WaveWatch-III model (Climate Forecast System Reanalysis hindcasts). An eastward decrease in wave height and period occurs from Portland to Wilson’s Promontory. This trend then reverses on the east coast. Across the west and central coasts, wave direction is dominated by south-west swells as influenced by strong westerly winds and mid-latitude low-pressure systems. On the east coast, wave direction becomes more variable, with added southerly, south-east and easterly components. The Southern Annular Mode influences wave climate variability on the west coast and is negatively correlated with storm frequency and wave direction. On the east coast, the El Niño–Southern Oscillation showed a strong positive correlation with wave height and a negative correlation with direction. This work provides a benchmark to compare to future changes. It will inform a higher-resolution analysis of the spatial correlation of wave conditions with climate processes to predict shoreline response.

Understanding the Spatial Distribution of Welfare Impacts of Global Warming on Agriculture and its Drivers

AbstractThis paper explores the interplay between the biophysical and economic geographies of climate change impacts on agriculture. It does so by bridging the extensive literature on climate impacts on yields and physical productivity in global crop production, with the literature on the role of adaptation through international trade in determining the consequences of climate change impacts. Unlike previous work in this area, instead of using a specific crop model or a set of models, we employ a statistical meta‐analysis that encompasses all studies available to the IPCC‐AR5 report. This permits us to isolate specific elements of the spatially heterogeneous biophysical geography of climate impacts, including the role of initial temperature, differential patterns of warming, and varying crop responses to warming across the globe. We combine these climate impact estimates with the Global Trade Analysis Project model of global trade in order to estimate the national welfare changes that are decomposed into three components: the direct (biophysical impact) contribution to welfare, the terms of trade effect, and the allocative efficiency effect. We find that when we remove the spatial variation in climate impacts, the terms of trade impacts are cut in half. Given the inherent heterogeneity of climate impacts in agriculture, this points to the important role of trade in distributing the associated welfare impacts. When we allow the biophysical impacts to vary across the empirically estimated uncertainty range taken from the meta‐analysis, we find that the welfare consequences are highly asymmetric, with much larger losses at the low end of the yield distribution. This interaction between the magnitude and heterogeneity of biophysical climate shocks and their welfare effects highlight the need for detailed representation of both in projecting climate change impacts.

Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

Spatial variability of urban climate in response to quantitative trait of land cover based on GWR model.

Land surface temperature and moisture are central components of the Earth's surface heat budget. China has experienced substantial land use/cover change that has led to deterioration of the urban microclimate, thus affecting global climate change. Understanding the spatial non-stationarity in the relationships between climate and land cover across a highly heterogeneous surface of urban landscapes is important for improving urban planning and management. This study used Landsat-8 OLI/TIRS data to explore the relationship of the three components (index-based built-up index (IBI); bare soil index (SI); and normalized difference vegetation index (NDVI)) with the urban climate (land surface temperature (LST) and land surface moisture (LSM)) using both a global model (ordinary least squares (OLS)) and a local model (geographically weighted regression (GWR)) for a megacity in Southeast China. The global regression results showed that there were significant positive correlations between the LST and the IBI and SI, while significant negative correlations were observed between the LST and the NDVI; opposite results were observed for the LSM. The IBI is the factor having the greatest impact on the LST, while the SI is among the most important factors for the LSM. The local regression results showed that the response of urban climate to land surface is affected greatly by water areas, but the role of the water areas is impacted by their size and surrounding landscape patterns. Moreover, the effects of vegetation and built-up land on the urban climate vary across locations with different wind patterns.

Partitioning global change: Assessing the relative importance of changes in climate and land cover for changes in avian distribution.

Understanding the relative impact of climate change and land cover change on changes in avian distribution has implications for the future course of avian distributions and appropriate management strategies. Due to the dynamic nature of climate change, our goal was to investigate the processes that shape species distributions, rather than the current distributional patterns. To this end, we analyzed changes in the distribution of Eastern Wood Pewees (Contopus virens) and Red‐eyed Vireos (Vireo olivaceus) from 1997 to 2012 using Breeding Bird Survey data and dynamic correlated‐detection occupancy models. We estimated the local colonization and extinction rates of these species in relation to changes in climate (hours of extreme temperature) and changes in land cover (amount of nesting habitat). We fit six nested models to partition the deviance explained by spatial and temporal components of land cover and climate. We isolated the temporal components of environmental variables because this is the essence of global change. For both species, model fit was significantly improved when we modeled vital rates as a function of spatial variation in climate and land cover. Model fit improved only marginally when we added temporal variation in climate and land cover to the model. Temporal variation in climate explained more deviance than temporal variation in land cover, although both combined only explained 20% (Eastern Wood Pewee) and 6% (Red‐eyed Vireo) of temporal variation in vital rates. Our results showing a significant correlation between initial occupancy and environmental covariates are consistent with biological expectation and previous studies. The weak correlation between vital rates and temporal changes in covariates indicated that we have yet to identify the most relevant components of global change influencing the distributions of these species and, more importantly, that spatially significant covariates are not necessarily driving temporal shifts in avian distributions.

Assessing spatial variability of extreme hot weather conditions in Hong Kong: A land use regression approach

The number of extreme hot weather events have considerably increased in Hong Kong in the recent decades. The complex urban context of Hong Kong leads to a significant intra-urban spatial variability in climate. Under such circumstance, a spatial understanding of extreme hot weather condition is urgently needed for heat risk prevention and public health actions. In this study, the extreme hot weather events of Hong Kong were quantified and measured using two indicators – very hot day hours (VHDHs) and hot night hours (HNHs) which were counted based on the summertime hourly-resolved air temperature data from a total of 40 weather stations (WSs) from 2011 to 2015. Using the VHDHs and HNHs at the locations of the 40 WSs as the outcome variables, land use regression (LUR) models are developed to achieve a spatial understanding of the extreme hot weather conditions in Hong Kong. Land surface morphology was quantified as the predictor variables in LUR modelling. A total of 167 predictor variables were considered in the model development process based on a stepwise multiple linear regression (MLR). The performance of resultant LUR models was evaluated via cross validation. VHDHs and HNHs were mapped at the community level for Hong Kong. The mapping results illustrate a significant spatial variation in the extreme hot weather conditions of Hong Kong in both the daytime and nighttime, which indicates that the spatial variation of land use configurations must be considered in the risk assessment and corresponding public health management associated with the extreme hot weather.

A Window into Africa’s Past Hydroclimates: The SISAL_v1 Database Contribution

Africa spans the hemispheres from temperate region to temperate region and has a long history of hominin evolution. Although the number of Quaternary palaeoclimatic records from the continent is increasing, much of the history of spatial and temporal climatic variability is still debated. Speleothems, as archives of terrestrial hydroclimate variability, can help reveal this history. Here we review the progress made to date, with a focus on the first version of the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database. The geology of Africa has limited development of large karst regions to four areas: along the northern coast bordering the Mediterranean, eastern Africa and the Horn of Africa, southwestern Africa and southern Africa. Exploitation of the speleothem palaeoclimate archives in these regions is uneven, with long histories of research, e.g., in South Africa, but large areas with no investigations such as West Africa. Consequently, the evidence of past climate change reviewed here is irregularly sampled in both time and space. Nevertheless, we show evidence of migration of the monsoon belt, with enhanced rainfall during interglacials observed in northeast Africa, southern Arabia and the northern part of southern Africa. Evidence from eastern Africa indicates significant decadal and centennial scale rainfall variability. In northwestern and southern Africa, precession and eccentricity influence speleothem growth, largely through changing synoptic storm activity.

Toward an integrated watershed zoning framework based on the spatio-temporal variability of land-cover and climate: Application in the Volta river basin

This article details a watershed regionalization approach which uses the concept of entropy in combination with the k-means clusters analysis. The regionalization approach aims to subdivide a watershed into relatively uniform zones based on the spatial variability of the local climate and land-covers. A case study is presented to illustrate the approach and outline the environmental implications of the outcomes. Especially, the study reports an application in the Volta river basin which is a transnational watershed, shared by six different countries in West Africa. Over years, the transboundary status of the Volta watershed seems to have exacerbated its environmental challenges, because the environmental policies in the six countries do not necessarily complement. Subsequently, it is desirable to envision unified scientific tools to support the management platform of the Volta basin. To date, the literature on the Volta has virtually neglected this aspect. Hence, this case study is timely as it intends to create a unified zoning system for the Volta river basin. In the study, formulations of entropy theory and k-means clustering were jointly applied to 16-years gridded time-series of monthly leaf area index, precipitation, and temperature across the Volta basin. Based on a clustering optimization criterion, a total of five zones were identified then the related land-cover and climatic patterns were comparatively analyzed. Significant environmental contrasts were diagnosed then specificities were pinpointed for each zone. A comparison of the new zones with an existing macro-scale ecoregion shows similarities which sustain the capacity of the regionalization approach to capturing meaningful biophysical signals. Hence, the zoning technique may be valued for further applications in environmental management.

Avian predation intensity as a driver of clinal variation in colour morph frequency.

Phenotypic variation provides the framework for natural selection to work upon, enabling adaptive evolution. One of the most discernible manifestations of phenotypic variability is colour variation. When this variation is discrete, genetically based colour pattern morphs occur simultaneously within a population. Why and how colour polymorphisms are maintained is an evolutionary puzzle. Several evolutionary drivers have been hypothesized as influencing clinal patterns of morph frequency, with spatial variation in climate and predation being considered especially important. Despite this, no study has examined both of their roles simultaneously. The aims of this study were to: (a) examine the covariation of physiology, environmental variables and colouration at a local scale; and (b) determine if these factors and their interplay explain broad clinal variation in morph frequency. We used the lizard Liopholis whitii as a model system, as this species displays a discrete, heritable polymorphism for colour pattern (plain-backed, patterned morphs) whose morph frequency varies latitudinally. We measured reflectance, field activity temperatures and microhabitat structure to test for differences in crypsis, thermal biology and microhabitat selection of patterned and plain-backed morphs within a single population where colour morphs occur sympatrically. We then used data from the literature to perform a broad-scale analysis to identify whether these factors also explained the latitudinal variation of morph frequency in this species. At the local scale, plain-backed morphs were found to be less cryptic than patterned morphs while no other differences were detected in terms of thermal biology, dorsal reflectance and microhabitat use. At a broader scale, predation was the most influential factor mediating morph frequency across latitudes. However, the observed pattern of morph frequency is opposite to what the modelling results suggest in that the incidence of the least cryptic morph is highest where predation pressure is most severe. Clinal variation in the level of background matching between morphs or the potential reproductive advantage by the plain-backed morph may, instead, be driving the observed morph frequency. Together, these results provide key insights into the evolution of local adaptation as well as the ecological forces involved in driving the dynamics of colour polymorphism.

Impacts of climate change on infestations of Dubas bug (Ommatissus lybicus Bergevin) on date palms in Oman.

Climate change has determined shifts in distributions of species and is likely to affect species in the future. Our study aimed to (i) demonstrate the linkage between spatial climatic variability and the current and historical Dubas bug (Ommatissus lybicus Bergevin) distribution in Oman and (ii) model areas becoming highly suitable for the pest in the future. The Dubas bug is a pest of date palm trees that can reduce the crop yield by 50% under future climate scenarios in Oman. Projections were made in three species distribution models; generalized linear model, maximum entropy, boosted regression tree using of four global circulation models (GCMs) (a) HadGEM2, (b) CCSM4, (c) MIROC5 and (d) HadGEM2-AO, under four representative concentration pathways (2.6, 4.5, 6.0 and 8.5) for the years 2050 and 2070. We utilized the most commonly used threshold of maximum sensitivity + specificity for classifying outputs. Results indicated that northern Oman is currently at great risk of Dubas bug infestations (highly suitable climatically) and the infestations level will remain high in 2050 and 2070. Other non-climatic integrated pest management methods may be greater value than climatic parameters for monitoring infestation levels, and may provide more effective strategies to manage Dubas bug infestations in Oman. This would ensure the continuing competitiveness of Oman in the global date fruit market and preserve national yields.