Spatial heterogeneity in climate change effects decouples the long‐term dynamics of wild reindeer populations in the high Arctic

The ‘Moran effect’ predicts that dynamics of populations of a species are synchronized over similar distances as their environmental drivers. Strong population synchrony reduces species viability, but spatial heterogeneity in density dependence, the environment, or its ecological responses may decouple dynamics in space, preventing extinctions. How such heterogeneity buffers impacts of global change on large‐scale population dynamics is not well studied. Here, we show that spatially autocorrelated fluctuations in annual winter weather synchronize wild reindeer dynamics across high‐Arctic Svalbard, while, paradoxically, spatial variation in winter climate trends contribute to diverging local population trajectories. Warmer summers have improved the carrying capacity and apparently led to increased total reindeer abundance. However, fluctuations in population size seem mainly driven by negative effects of stochastic winter rain‐on‐snow (ROS) events causing icing, with strongest effects at high densities. Count data for 10 reindeer populations 8–324 km apart suggested that density‐dependent ROS effects contributed to synchrony in population dynamics, mainly through spatially autocorrelated mortality. By comparing one coastal and one ‘continental’ reindeer population over four decades, we show that locally contrasting abundance trends can arise from spatial differences in climate change and responses to weather. The coastal population experienced a larger increase in ROS, and a stronger density‐dependent ROS effect on population growth rates, than the continental population. In contrast, the latter experienced stronger summer warming and showed the strongest positive response to summer temperatures. Accordingly, contrasting net effects of a recent climate regime shift—with increased ROS and harsher winters, yet higher summer temperatures and improved carrying capacity—led to negative and positive abundance trends in the coastal and continental population respectively. Thus, synchronized population fluctuations by climatic drivers can be buffered by spatial heterogeneity in the same drivers, as well as in the ecological responses, averaging out climate change effects at larger spatial scales.     

The effects of climate data precision on fitting and projecting species niche models

Biosecurity agencies are particularly concerned to know the potential distribution of invasive alien species under present, and to a lesser extent, future climates; expensive decisions can hinge upon the degree of perceived threat a pest species poses. Climate‐based niche modelling techniques are available to inform these decisions. These tools now regularly employ gridded climate datasets of moderate spatial resolution (0.5 degree), though biosecurity decision‐makers continually seek greater spatial precision in the risk map products. Various splining techniques are capable of generating gridded climate datasets approaching the precision limits imposed by the availability of digital elevation model data. As the spatial precision of climate datasets increases, more detailed effects of topographic relief become apparent in the climatic data. When these datasets are used to develop and apply species niche models, the climate data is spatially intersected with species location data to infer relationships between the climate and the species’ geographic distribution. Here we investigate the effect of changing climate precision on projections of species’ niche models developed with CLIMEX, including the effect of upscaling and downscaling the outputs. We found that there were noticeable increases in sensitivity in models developed using more precise climate datasets. The largest differences in projections were noted where species range limits coincided with regions of strong climatic gradients such as where there was marked topographic relief in relation to the spatial precision of the climatic dataset. Upscaling (fitting a model with a fine resolution dataset and then projecting the results with a coarser grid), tended to produce smaller potential ranges for a species, albeit at the cost of model sensitivity. Downscaling had the opposite effect, identifying additional, mostly marginally climatically suitable habitat. It remains unclear how sensitive the fine resolution results are to the number and spatial arrangement of input location records used to build the model. The results indicate some benefits of improving the spatial resolution of climate datasets, though not at the expense of climatic data accuracy. Decision‐makers should be mindful of the inherent uncertainties in these models, and modellers have a responsibility to identify and convey these uncertainties to their intended audience.     

Projecting the future of an alpine ungulate under climate change scenarios

Climate change represents a primary threat to species persistence and biodiversity at a global scale. Cold adapted alpine species are especially sensitive to climate change and can offer key “early warning signs” about deleterious effects of predicted change. Among mountain ungulates, survival, a key determinant of demographic performance, may be influenced by future climate in complex, and possibly opposing ways. Demographic data collected from 447 mountain goats in 10 coastal Alaska, USA, populations over a 37‐year time span indicated that survival is highest during low snowfall winters and cool summers. However, general circulation models (GCMs) predict future increase in summer temperature and decline in winter snowfall. To disentangle how these opposing climate‐driven effects influence mountain goat populations, we developed an age‐structured population model to project mountain goat population trajectories for 10 different GCM/emissions scenarios relevant for coastal Alaska. Projected increases in summer temperature had stronger negative effects on population trajectories than the positive demographic effects of reduced winter snowfall. In 5 of the 10 GCM/representative concentration pathway (RCP) scenarios, the net effect of projected climate change was extinction over a 70‐year time window (2015–2085); smaller initial populations were more likely to go extinct faster than larger populations. Using a resource selection modeling approach, we determined that distributional shifts to higher elevation (i.e., “thermoneutral”) summer range was unlikely to be a viable behavioral adaptation strategy; due to the conical shape of mountains, summer range was expected to decline by 17%–86% for 7 of the 10 GCM/RCP scenarios. Projected declines of mountain goat populations are driven by climate‐linked bottom‐up mechanisms and may have wide ranging implications for alpine ecosystems. These analyses elucidate how projected climate change can negatively alter population dynamics of a sentinel alpine species and provide insight into how demographic modeling can be used to assess risk to species persistence.     

Human Neutral Genetic Variation and Forensic STR Data

The forensic genetics field is generating extensive population data on polymorphism of short tandem repeats (STR) markers in globally distributed samples. In this study we explored and quantified the informative power of these datasets to address issues related to human evolution and diversity, by using two online resources: an allele frequency dataset representing 141 populations summing up to almost 26 thousand individuals; a genotype dataset consisting of 42 populations and more than 11 thousand individuals. We show that the genetic relationships between populations based on forensic STRs are best explained by geography, as observed when analysing other worldwide datasets generated specifically to study human diversity. However, the global level of genetic differentiation between populations (as measured by a fixation index) is about half the value estimated with those other datasets, which contain a much higher number of markers but much less individuals. We suggest that the main factor explaining this difference is an ascertainment bias in forensics data resulting from the choice of markers for individual identification. We show that this choice results in average low variance of heterozygosity across world regions, and hence in low differentiation among populations. Thus, the forensic genetic markers currently produced for the purpose of individual assignment and identification allow the detection of the patterns of neutral genetic structure that characterize the human population but they do underestimate the levels of this genetic structure compared to the datasets of STRs (or other kinds of markers) generated specifically to study the diversity of human populations.     

Annual global mean temperature explains reproductive success in a marine vertebrate from 1955 to 2010

The salient feature of anthropogenic climate change over the last century has been the rise in global mean temperature. However, global mean temperature is not used as an explanatory variable in studies of population‐level response to climate change, perhaps because the signal‐to‐noise ratio of this gross measure makes its effect difficult to detect in any but the longest of datasets. Using a population of Leach's storm‐petrels breeding in the Bay of Fundy, we tested whether local, regional, or global temperature measures are the best index of reproductive success in the face of climate change in species that travel widely between and within seasons. With a 56‐year dataset, we found that annual global mean temperature (AGMT) was the single most important predictor of hatching success, more so than regional sea surface temperatures (breeding season or winter) and local air temperatures at the nesting colony. Storm‐petrel reproductive success showed a quadratic response to rising temperatures, in that hatching success increased up to some critical temperature, and then declined when AGMT exceeded that temperature. The year at which AGMT began to consistently exceed that critical temperature was 1988. Importantly, in this population of known‐age individuals, the impact of changing climate was greatest on inexperienced breeders: reproductive success of inexperienced birds increased more rapidly as temperatures rose and declined more rapidly after the tipping point than did reproductive success of experienced individuals. The generality of our finding that AGMT is the best predictor of reproductive success in this system may hinge on two things. First, an integrative global measure may be best for species in which individuals move across an enormous spatial range, especially within seasons. Second, the length of our dataset and our capacity to account for individual‐ and age‐based variation in reproductive success increase our ability to detect a noisy signal.     

Population distribution, settlement patterns and accessibility across Africa in 2010

The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.     

Population genetics of south European Atlantic salmon under global change

Populations at the edge of species distributions are especially vulnerable to climate change. Genetic changes as well as modification of their population structure are expected as reactions to global warming. Atlantic salmon ( Salmo salar ) inhabiting south France has been chosen as a model for studying the effect of global warming in marginal populations during the last 15 years. Increased gene flow between neighboring populations and dichotomy of maturation age between sexes have been identified as two main population changes significantly associated with high values of the North Atlantic Oscillation index, a global climate indicator. Although occurrence of isolated populations in each river (or even tributary) is a paradigm for this species, at least in northern areas, increased gene flow between rivers is forecasted as long as climate warming increases, favoring metapopulations at regional level.     

Integrating bioclimate with population models to improve forecasts of species extinctions under climate change

Climate change is already affecting species worldwide, yet existing methods of risk assessment have not considered interactions between demography and climate and their simultaneous effect on habitat distribution and population viability. To address this issue, an international workshop was held at the University of Adelaide in Australia, 25–29 May 2009, bringing leading species distribution and population modellers together with plant ecologists. Building on two previous workshops in the UK and Spain, the participants aimed to develop methodological standards and case studies for integrating bioclimatic and metapopulation models, to provide more realistic forecasts of population change, habitat fragmentation and extinction risk under climate change. The discussions and case studies focused on several challenges, including spatial and temporal scale contingencies, choice of predictive climate, land use, soil type and topographic variables, procedures for ensemble forecasting of both global climate and bioclimate models and developing demographic structures that are realistic and species-specific and yet allow generalizations of traits that make species vulnerable to climate change. The goal is to provide general guidelines for assessing the Red-List status of large numbers of species potentially at risk, owing to the interactions of climate change with other threats such as habitat destruction, overexploitation and invasive species.     

Climate change and coastal waterbird populations – past declines and future impacts

Considerable attention has been given to the impact of climate change on avian populations over the last decade. In this paper we examine two issues with respect to coastal bird populations in the UK: (1) is there any evidence that current populations are declining due to climate change, and (2) how might we predict the response of populations in the future? We review the cause of population decline in two species associated with saltmarsh habitats. The abundance of Common Redshank Tringa totanus breeding on saltmarsh declined by about 23% between the mid-1980s and mid-1990s, but the decline appears to have been caused by an increase in grazing pressure. The number of Twite Carduelis flavirostris wintering on the coast of East Anglia has declined dramatically over recent decades; there is evidence linking this decline with habitat loss but a causal role for climate change is unclear. These examples illustrate that climate change could be having population-level impacts now, but also show that it is dangerous to become too narrowly focused on single issues affecting coastal birds. Making predictions about how populations might respond to future climate change depends on an adequate understanding of important ecological processes at an appropriate spatial scale. We illustrate this with recent work conducted on the Icelandic population of Black-tailed Godwits Limosa limosa islandica that shows large-scale regulatory processes. Most predictive models to date have focused on local populations (single estuary or a group of neighbouring estuaries). We discuss the role such models might play in risk assessment, and the need for them to be linked to larger-scale ecological processes. We argue that future work needs to focus on spatial scale issues and on linking physical models of coastal environments with important ecological processes.     

Population Differentiation in Locomotor Performance and the Potential Response of a Terrestrial Organism to Global Environmental Change

SYNOPSIS. Models of global climate change predict an increasein the mean surface temperature between 1.5° and 4.5°Cby the middle of the next century. Even a moderate increaseof 3°C is likely to result in a shift in the distributionof North American habitat types and vegetational associations,either in latitude or elevation or both. The challenge to ConservationBiology is to predict the responses of terrestrial organismsto the expected alteration in habitats and ecosystems. Recentbiophysical models have been expanded to demonstrate the intimateassociations between the thermal environment, organismal physiologyand ecology. Thus, the expected turnover in habitats may havea profound influence on the distribution of organisms. I describeone possible approach that couples temporal and spatial variationin an ecologically relevant physiological trait, locomotoryperformance, in a widespread species of lizard, Urosaurus ornatus,to predict the expected response of species to global change. Estimates of maximum velocity and endurance capacity were obtainedfrom 16 populations throughout the range of U. ornatus. Informationon spatial variation was supplemented with data on temporalvariation spanning an eight year period from a single referencepopulation. I used thesedata to address two questions: 1) isthere an association between locomotory performance and theexpected habitat changes predicted from global climate modelsand 2) is there sufficient variation within a population torespond to habitat modification. Populations of U. ornatus varied significantly in sprint speedand stamina. Several environmental factors expected to correlatewith global climatechange were evaluated using the patternsof variation in locomotor performance. Results from this studysuggest that high elevation populations found in ponderosa pineforests should be most susceptible to changes in climate. Within-populationvariation was found to span the range of variation seen amongpopulations and was sensitive to temporal variation in climaticconditions. Given the expected and rapid change in environments,small, ectothermic, terrestrial species may not have the abilityto modify their geographic distribution. However, the resultspresented here suggest that only certain populations are atrisk; yet the evolutionary response of the population may belong relative to the rate of environment change.     

Geographic disparities and moral hazards in the predicted impacts of climate change on human populations

Aim It has been qualitatively understood for a long time that climate change will have widely varying effects on human well‐being in different regions of the world. The spatial complexities underlying our relationship to climate and the geographical disparities in human demographic change have, however, precluded the development of global indices of the predicted regional impacts of climate change on humans. Humans will be most negatively affected by climate change in regions where populations are strongly dependent on climate and favourable climatic conditions decline. Here we use the relationship between the distribution of human population density and climate as a basis to develop the first global index of predicted impacts of climate change on human populations. Location Global. Methods We use spatially explicit models of the present relationship between human population density and climate along with forecasted climate change to predict climate vulnerabilities over the coming decades. We then globally represent regional disparities in human population dynamics estimated with our ecological niche model and with a demographic forecast and contrast these disparities with CO₂ emissions data to quantitatively evaluate the notion of moral hazard in climate change policies. Results Strongly negative impacts of climate change are predicted in Central America, central South America, the Arabian Peninsula, Southeast Asia and much of Africa. Importantly, the regions of greatest vulnerability are generally distant from the high‐latitude regions where the magnitude of climate change will be greatest. Furthermore, populations contributing the most to greenhouse gas emissions on a per capita basis are unlikely to experience the worst impacts of climate change, satisfying the conditions for a moral hazard in climate change policies. Main conclusions Regionalized analysis of relationships between distribution of human population density and climate provides a novel framework for developing global indices of human vulnerability to climate change. The predicted consequences of climate change on human populations are correlated with the factors causing climate change at the regional level, providing quantitative support for many qualitative statements found in international climate change assessments.     

Masting in ponderosa pine: comparisons of pollen and seed over space and time

Many plant species exhibit variable and synchronized reproduction, or masting, but less is known of the spatial scale of synchrony, effects of climate, or differences between patterns of pollen and seed production. We monitored pollen and seed cone production for seven Pinus ponderosa populations (607 trees) separated by up to 28?km and 1,350?m in elevation in Boulder County, Colorado, USA for periods of 4?C31?years for a mean per site of 8.7?years for pollen and 12.1 for seed cone production. We also analyzed climate data and a published dataset on 21?years of seed production for an eighth population (Manitou) 100?km away. Individual trees showed high inter-annual variation in reproduction. Synchrony was high within populations, but quickly became asynchronous among populations with a combination of increasing distance and elevational difference. Inter-annual variation in temperature and precipitation had differing influences on seed production for Boulder County and Manitou. We speculate that geographically variable effects of climate on reproduction arise from environmental heterogeneity and population genetic differentiation, which in turn result in localized synchrony. Although individual pines produce pollen and seed, only one-third of the covariation within trees was shared. As compared to seed cones, pollen had lower inter-annual variation at the level of the individual tree and was more synchronous. However, pollen and seed production were similar with respect to inter-annual variation at the population level, spatial scales of synchrony and associations with climate. Our results show that strong masting can occur at a localized scale, and that reproductive patterns can differ between pollen and seed cone production in a hermaphroditic plant.     

Matching habitat choice promotes species persistence under climate change

Species may survive under contemporary climate change by either shifting their range or adapting locally to the warmer conditions. Theoretical and empirical studies recently underlined that dispersal, the central mechanism behind these responses, may depend on the match between an individuals’ phenotype and local environment. Such matching habitat choice is expected to induce an adaptive gene flow, but it now remains to be studied whether this local process could promote species’ responses to climate change. Here, we investigate this by developing an individual‐based model including either random dispersal or temperature‐dependent matching habitat choice. We monitored population composition and distribution through space and time under climate change. Relative to random dispersal, matching habitat choice induced an adaptive gene flow that lessened spatial range loss during climate warming by improving populations’ viability within the range (i.e. limiting range fragmentation) and by facilitating colonization of new habitats at the cold margin. The model even predicted range contraction under random dispersal but range expansion under optimal matching habitat choice. These benefits of matching habitat choice for population persistence mostly resulted from adaptive immigration decision and were greater for populations with larger dispersal distance and higher emigration probability. We also found that environmental stochasticity resulted in suboptimal matching habitat choice, decreasing the benefits of this dispersal mode under climate change. However population persistence was still better under suboptimal matching habitat choice than under random dispersal. Our results highlight the urgent need to implement more realistic mechanisms of dispersal such as matching habitat choice into models predicting the impacts of ongoing climate change on biodiversity.     

Range margin populations show high climate adaptation lags in European trees

How populations of long‐living species respond to climate change depends on phenotypic plasticity and local adaptation processes. Marginal populations are expected to have lags in adaptation (i.e. differences between the climatic optimum that maximizes population fitness and the local climate) because they receive pre‐adapted alleles from core populations preventing them from reaching a local optimum in their climatically marginal habitat. Yet, whether adaptation lags in marginal populations are a common feature across phylogenetically and ecologically different species and how lags can change with climate change remain unexplored. To test for range‐wide patterns of phenotypic variation and adaptation lags of populations to climate, we (a) built model ensembles of tree height accounting for the climate of population origin and the climate of the site for 706 populations monitored in 97 common garden experiments covering the range of six European forest tree species; (b) estimated populations' adaptation lags as the differences between the climatic optimum that maximizes tree height and the climate of the origin of each population; (c) identified adaptation lag patterns for populations coming from the warm/dry and cold/wet margins and from the distribution core of each species range. We found that (a) phenotypic variation is driven by either temperature or precipitation; (b) adaptation lags are consistently higher in climatic margin populations (cold/warm, dry/wet) than in core populations; (c) predictions for future warmer climates suggest adaptation lags would decrease in cold margin populations, slightly increasing tree height, while adaptation lags would increase in core and warm margin populations, sharply decreasing tree height. Our results suggest that warm margin populations are the most vulnerable to climate change, but understanding how these populations can cope with future climates depend on whether other fitness‐related traits could show similar adaptation lag patterns.     

Effects of soil water temperature on root hydraulic resistance of six species of Iberian pines

Abstract

The Iberian Peninsula hosts six native pine species, which are distributed according to an altitudinal gradient from coastal to mountain areas, close to 1000 m a.s.l. Root hydraulic responses are the key factors of spatial segregation of trees in response to environmental factors such as temperature and water availability, and they will be a determinant of future population and species spatial dynamics in a changing climate scenario. Root hydraulic responses to soil water temperatures ranging from 30°C to 0°C were compared for young plants of these six aforementioned species. Hydraulic resistance (Rh) increased for all species in response to temperature decrease. Mountain pines showed higher Rh values than coastal pines at all temperatures, and showed a more prompt and marked hydraulic response when temperatures dropped down. Data point out that mountain pines display a clear mechanism to avoid cold embolism and secondary water stress, while coastal species have a limited responsiveness to temperature changes due to scarce hydraulic regulation. These differences in hydraulic behaviour support the spatial segregation between mountain and coastal pines in the Iberian Peninsula, and will be one of the factors at the basis of the future shifts of species and populations that will be associated to climate change.     

Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets

This paper evaluates the opportunity provided by global interferometric radar datasets for monitoring deforestation, degradation and forest regrowth in tropical and semi-arid environments. The paper describes an easy to implement method for detecting forest spatial changes and estimating their magnitude. The datasets were acquired within space-borne high spatial resolutions radar missions at near-global scales thus being significant for monitoring systems developed under the United Framework Convention on Climate Change (UNFCCC). The approach presented in this paper was tested in two areas located in Indonesia and Australia. Forest change estimation was based on differences between a reference dataset acquired in February 2000 by the Shuttle Radar Topography Mission (SRTM) and TanDEM-X mission (TDM) datasets acquired in 2011 and 2013. The synergy between SRTM and TDM datasets allowed not only identifying changes in forest extent but also estimating their magnitude with respect to the reference through variations in forest height.     

Integrating spatial data and shorebird nesting locations to predict the potential future impact of global warming on coastal habitats: A case study on Farasan Islands,Saudi Arabia

One of the expected effects of the global warming is changing coastal habitats by accelerating the rate of sea level rise. Coastal habitats support large number of marine and wetland species including shorebirds (plovers, sandpipers and allies). In this study, we investigate how coastal habitats may be impacted by sea level rise in the Farasan Islands, Kingdom of Saudi Arabia. We use Kentish plover Charadrius alexandrinus – a common coastal breeding shorebird – as an ecological model species to predict the influence of sea level rise. We found that any rise of sea level is likely to inundate 11% of Kentish plover nests. In addition, 5% of the coastal areas of Farasan Islands, which support 26% of Kentish plover nests, will be flooded, if sea level rises by one metre. Our results are constrained by the availability of data on both elevation and bird populations. Therefore, we recommend follow-up studies to model the impacts of sea level rise using different elevation scenarios, and the establishment of a monitoring programme for breeding shorebirds and seabirds in Farasan Islands to assess the impact of climate change on their populations.     

Effects of climate on population fluctuations of ibex

Predicting the effects of the expected changes in climate on the dynamics of populations require that critical periods for climate‐induced changes in population size are identified. Based on time series analyses of 26 Swiss ibex (Capra ibex) populations, we show that variation in winter climate affected the annual changes in population size of most of the populations after accounting for the effects of density dependence and demographic stochasticity. In addition, precipitation during early summer also influenced the population fluctuations. This suggests that the major influences of climate on ibex population dynamics operated either through loss of individuals during winter or early summer, or through an effect on fecundity. However, spatial covariation in these climate variables was not able to synchronize the population fluctuations of ibex over larger distances, probably due to large spatial heterogeneity in the effects of single climate variables on different populations. Such spatial variation in the influence of the same climate variable on the local population dynamics suggests that predictions of influences of climate change need to account for local differences in population dynamical responses to climatic conditions.     

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate.     

A systematic review of vegetation phenology in Africa

The study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa's vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provide detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies.