Predicted wind and solar energy expansion has minimal overlap with multiple conservation priorities across global regions.

Protected areas and renewable energy generation are critical tools to combat biodiversity loss and climate change, respectively. Over the coming decades, expansion of the protected area network to meet conservation objectives will be occurring alongside rapid deployment of renewable energy infrastructure to meet climate targets, driving potential conflict for a finite land resource. Renewable energy infrastructure can have negative effects on wildlife, and co-occurrence may mean emissions targets are met at the expense of conservation objectives. Here, we assess current and projected overlaps of wind and solar photovoltaic installations and important conservation areas across nine global regions using spatially explicit wind and solar data and methods for predicting future renewable expansion. We show similar levels of co-occurrence as previous studies but demonstrate that once area is accounted for, previous concerns about overlaps in the Northern Hemisphere may be largely unfounded, although they are high in some biodiverse countries (e.g., Brazil). Future projections of overlap between the two land uses presented here are generally dependent on priority threshold and region and suggest the risk of future conflict can be low. We use the best available data on protected area degradation to corroborate this level of risk. Together, our findings indicate that while conflicts between renewables and protected areas inevitably do occur, renewables represent an important option for decarbonization of the energy sector that would not significantly affect area-based conservation targets if deployed with appropriate policy and regulatory controls.

The influence of the global electric power system on terrestrial biodiversity.

Given its total contribution to greenhouse gas emissions, the global electric power sector will be required to undergo a fundamental transformation over the next decades to limit anthropogenic climate change to below 2 °C. Implications for biodiversity of projected structural changes in the global electric power sector are rarely considered beyond those explicitly linked to climate change. This study uses a spatially explicit consumption-based accounting framework to examine the impact of demand for electric power on terrestrial vertebrate biodiversity globally. We demonstrate that the biodiversity footprint of the electric power sector is primarily within the territory where final demand for electric power resides, although there are substantial regional differences, with Europe displacing its biodiversity threat along international supply chains. The relationship between size of individual components of the electric power sector and threat to biodiversity indicates that a shift to nonfossil sources, such as solar and wind, could reduce pressures on biodiversity both within the territory where demand for power resides and along international supply chains. However, given the current levels of deployment of nonfossil sources of power, there is considerable uncertainty as to how the impacts of structural changes in the global electric power system will scale. Given the strong territorial link between demand and associated biodiversity impacts, development of strong national governance around the electric power sector represents a clear route to mitigate threats to biodiversity associated with efforts to decarbonize society over the coming century.

Global impacts of energy demand on the freshwater resources of nations.

The growing geographic disconnect between consumption of goods, the extraction and processing of resources, and the environmental impacts associated with production activities makes it crucial to factor global trade into sustainability assessments. Using an empirically validated environmentally extended global trade model, we examine the relationship between two key resources underpinning economies and human well--being-energy and freshwater. A comparison of three energy sectors (petroleum, gas, and electricity) reveals that freshwater consumption associated with gas and electricity production is largely confined within the territorial boundaries where demand originates. This finding contrasts with petroleum, which exhibits a varying ratio of territorial to international freshwater consumption, depending on the origin of demand. For example, although the United States and China have similar demand associated with the petroleum sector, international freshwater consumption is three times higher for the former than the latter. Based on mapping patterns of freshwater consumption associated with energy sectors at subnational scales, our analysis also reveals concordance between pressure on freshwater resources associated with energy production and freshwater scarcity in a number of river basins globally. These energy-driven pressures on freshwater resources in areas distant from the origin of energy demand complicate the design of policy to ensure security of fresh water and energy supply. Although much of the debate around energy is focused on greenhouse gas emissions, our findings highlight the need to consider the full range of consequences of energy production when designing policy.

Global evidence of positive impacts of freshwater biodiversity on fishery yields.

AIM: An often-invoked benefit of high biodiversity is the provision of ecosystem services. However, evidence for this is largely based on data from small-scale experimental studies of relationships between biodiversity and ecosystem function that may have little relevance to real-world systems. Here, large-scale biodiversity datasets are used to test the relationship between the yield of inland capture fisheries and species richness from 100 countries. LOCATION: Inland waters of Africa, Europe and parts of Asia. METHODS: A multimodel inference approach was used to assess inland fishery yields at the country level against species richness, waterside human population, area, elevation and various climatic variables, to determine the relative importance of species richness to fisheries yields compared with other major large-scale drivers. Secondly, the mean decadal variation in fishery yields at the country level for 1981-2010 was regressed against species richness to assess if greater diversity reduces the variability in yields over time. RESULTS: Despite a widespread reliance on targeting just a few species of fish, freshwater fish species richness is highly correlated with yield (R2 = 0.55) and remains an important and statistically significant predictor of yield once other macroecological drivers are controlled for. Freshwater richness also has a significant negative relationship with variability of yield over time in Africa (R2 = 0.16) but no effect in Europe. MAIN CONCLUSIONS: The management of inland waters should incorporate the protection of freshwater biodiversity, particularly in countries with the highest-yielding inland fisheries as these also tend to have high freshwater biodiversity. As these results suggest a link between biodiversity and stable, high-yielding fisheries, an important win-win outcome may be possible for food security and conservation of freshwater ecosystems. However, findings also highlight the urgent need for more data to fully understand and monitor the contribution of biodiversity to inland fisheries globally.

Vulnerability of ecosystems to climate change moderated by habitat intactness.

The combined effects of climate change and habitat loss represent a major threat to species and ecosystems around the world. Here, we analyse the vulnerability of ecosystems to climate change based on current levels of habitat intactness and vulnerability to biome shifts, using multiple measures of habitat intactness at two spatial scales. We show that the global extent of refugia depends highly on the definition of habitat intactness and spatial scale of the analysis of intactness. Globally, 28% of terrestrial vegetated area can be considered refugia if all natural vegetated land cover is considered. This, however, drops to 17% if only areas that are at least 50% wilderness at a scale of 48×48 km are considered and to 10% if only areas that are at least 50% wilderness at a scale of 4.8×4.8 km are considered. Our results suggest that, in regions where relatively large, intact wilderness areas remain (e.g. Africa, Australia, boreal regions, South America), conservation of the remaining large-scale refugia is the priority. In human-dominated landscapes, (e.g. most of Europe, much of North America and Southeast Asia), focusing on finer scale refugia is a priority because large-scale wilderness refugia simply no longer exist. Action to conserve such refugia is particularly urgent since only 1 to 2% of global terrestrial vegetated area is classified as refugia and at least 50% covered by the global protected area network.

Reconciling biodiversity and carbon conservation.

Climate change is leading to the development of land-based mitigation and adaptation strategies that are likely to have substantial impacts on global biodiversity. Of these, approaches to maintain carbon within existing natural ecosystems could have particularly large benefits for biodiversity. However, the geographical distributions of terrestrial carbon stocks and biodiversity differ. Using conservation planning analyses for the New World and Britain, we conclude that a carbon-only strategy would not be effective at conserving biodiversity, as have previous studies. Nonetheless, we find that a combined carbon-biodiversity strategy could simultaneously protect 90% of carbon stocks (relative to a carbon-only conservation strategy) and > 90% of the biodiversity (relative to a biodiversity-only strategy) in both regions. This combined approach encapsulates the principle of complementarity, whereby locations that contain different sets of species are prioritised, and hence disproportionately safeguard localised species that are not protected effectively by carbon-only strategies. It is efficient because localised species are concentrated into small parts of the terrestrial land surface, whereas carbon is somewhat more evenly distributed; and carbon stocks protected in one location are equivalent to those protected elsewhere. Efficient compromises can only be achieved when biodiversity and carbon are incorporated together within a spatial planning process.

What is macroecology?

The symposium 'What is Macroecology?' was held in London on 20 June 2012. The event was the inaugural meeting of the Macroecology Special Interest Group of the British Ecological Society and was attended by nearly 100 scientists from 11 countries. The meeting reviewed the recent development of the macroecological agenda. The key themes that emerged were a shift towards more explicit modelling of ecological processes, a growing synthesis across systems and scales, and new opportunities to apply macroecological concepts in other research fields.

Balancing alternative land uses in conservation prioritization.

Pressure on ecosystems to provide various different and often conflicting services is immense and likely to increase. The impacts and success of conservation prioritization will be enhanced if the needs of competing land uses are recognized at the planning stage. We develop such methods and illustrate them with data about competing land uses in Great Britain, with the aim of developing a conservation priority ranking that balances between needs of biodiversity conservation, carbon storage, agricultural value, and urban development potential. While both carbon stocks and biodiversity are desirable features from the point of view of conservation, they compete with the needs of agriculture and urban development. In Britain the greatest conflicts exist between biodiversity and urban areas, while the largest carbon stocks occur mostly in Scotland in areas with low agricultural or urban pressure. In our application, we were able successfully to balance the spatial allocation of alternative land uses so that conflicts between them were much smaller than had they been developed separately. The proposed methods and software, Zonation, are applicable to structurally similar prioritization problems globally.

Spatial covariation between freshwater and terrestrial ecosystem services.

To inform the design and implementation of land-use policies that consider the variety of goods and services people derive from ecosystems, it is essential to understand spatial patterns of individual services, how multiple services relate to each other, and how these relationships vary across spatial scales and localities. Despite the importance of freshwater as a determinant of regional economic and human demographic patterns, there are surprisingly few studies that map the provision of a range of services associated with the quality of the aquatic environment. Here we examine relationships between indicators of riverine water and associated habitat quality, freshwater biodiversity, three terrestrial ecosystem services, and terrestrial biodiversity across England and Wales. The results indicate strong associations between our indicators of freshwater services. However, a comparison of these indicators of freshwater services with other ecosystem services (carbon storage, agricultural production, recreation) and biodiversity of species of conservation concern in the surrounding terrestrial landscape shows no clear relationships. While there are potential policy "win-wins" for the protection of multiple services shown by associations between indicators of freshwater services and carbon storage in upland areas of Britain, the other ecosystem services showed either negative or no relationships with the indicators of freshwater services. We also consider the influence that spatial scale has on these relationships using River Basin Districts. Our results indicate that relationships between indicators of services can change dramatically depending on the societal pressures and other regional conditions. Thus, the delivery of multiple ecosystem services requires the development of regional strategies, or of national strategies that take account of regional variation.

Global hotspots of species richness are not congruent with endemism or threat.

Biodiversity hotspots have a prominent role in conservation biology, but it remains controversial to what extent different types of hotspot are congruent. Previous studies were unable to provide a general answer because they used a single biodiversity index, were geographically restricted, compared areas of unequal size or did not quantitatively compare hotspot types. Here we use a new global database on the breeding distribution of all known extant bird species to test for congruence across three types of hotspot. We demonstrate that hotspots of species richness, threat and endemism do not show the same geographical distribution. Only 2.5% of hotspot areas are common to all three aspects of diversity, with over 80% of hotspots being idiosyncratic. More generally, there is a surprisingly low overall congruence of biodiversity indices, with any one index explaining less than 24% of variation in the other indices. These results suggest that, even within a single taxonomic class, different mechanisms are responsible for the origin and maintenance of different aspects of diversity. Consequently, the different types of hotspots also vary greatly in their utility as conservation tools.

The current and future uses of machine learning in ecosystem service research.

Machine learning (ML) expands traditional data analysis and presents a range of opportunities in ecosystem service (ES) research, offering rapid processing of 'big data' and enabling significant advances in data description and predictive modelling. Descriptive ML techniques group data with little or no prior domain specific assumptions; they can generate hypotheses and automatically sort data prior to other analyses. Predictive ML techniques allow for the predictive modelling of highly non-linear systems where casual mechanisms are poorly understood, as is often the case for ES. We conducted a review to explore how ML is used in ES research and to identify and quantify trends in the different ML approaches that are used. We reviewed 308 peer-reviewed publications and identified that ES studies implemented machine learning techniques in data description (64%; n = 308) and predictive modelling (44%), with some papers containing both categories. Classification and Regression Trees were the most popular techniques (60%), but unsupervised learning techniques were also used for descriptive tasks such as clustering to group or split data without prior assumptions (19%). Whilst there are examples of ES publications that apply ML with rigour, many studies do not have robust or repeatable methods. Some studies fail to report model settings (43%) or software used (28%), and many studies do not report carrying out any form of model hyperparameter tuning (67%) or test model generalisability (59%). Whilst studies use ML to analyse very large and complex datasets, ES research is generally not taking full advantage of the capacity of ML to model big data (1138 medium number of data points; 13 median quantity of variables). There is great further opportunity to utilise ML in ES research, to make better use of big data and to develop detailed modelling of spatial-temporal dynamics that meet stakeholder demands.

Harmonised global datasets of wind and solar farm locations and power.

Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet's limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.

Impacts of rising temperatures and farm management practices on global yields of 18 crops.

Understanding the impact of changes in temperature and precipitation on crop yields is a vital step in developing policy and management options to feed the world. As most existing studies are limited to a few staple crops, we implemented global statistical models to examine the influence of weather and management practices on the yields of 18 crops, accounting for 70% of crop production by area and 65% by calorific intake. Focusing on the impact of temperature, we found considerable heterogeneity in the responses of yields across crops and countries. Irrigation was found to alleviate negative implications from temperature increases. Countries where increasing temperature causes the most negative impacts are typically the most food insecure, with the lowest calorific food supply and average crop yield. International action must be coordinated to raise yields in these countries through improvement and modernization of agricultural practices to counteract future adverse impacts of climate change.

Ensembles of ecosystem service models can improve accuracy and indicate uncertainty.

Many ecosystem services (ES) models exist to support sustainable development decisions. However, most ES studies use only a single modelling framework and, because of a lack of validation data, rarely assess model accuracy for the study area. In line with other research themes which have high model uncertainty, such as climate change, ensembles of ES models may better serve decision-makers by providing more robust and accurate estimates, as well as provide indications of uncertainty when validation data are not available. To illustrate the benefits of an ensemble approach, we highlight the variation between alternative models, demonstrating that there are large geographic regions where decisions based on individual models are not robust. We test if ensembles are more accurate by comparing the ensemble accuracy of multiple models for six ES against validation data across sub-Saharan Africa with the accuracy of individual models. We find that ensembles are better predictors of ES, being 5.0-6.1% more accurate than individual models. We also find that the uncertainty (i.e. variation among constituent models) of the model ensemble is negatively correlated with accuracy and so can be used as a proxy for accuracy when validation is not possible (e.g. in data-deficient areas or when developing scenarios). Since ensembles are more robust, accurate and convey uncertainty, we recommend that ensemble modelling should be more widely implemented within ES science to better support policy choices and implementation.

Identifying Agricultural Frontiers for Modeling Global Cropland Expansion.

The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992-2015). Deviations from models of cropland extent in 1992 ("frontierness") can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models.

Ecosystem service benefits of contrasting conservation strategies in a human-dominated region.

The hope among policy-makers and scientists alike is that conservation strategies designed to protect biodiversity also provide direct benefits to people by protecting other vital ecosystem services. The few studies that have examined the delivery of ecosystem services by existing conservation efforts have concentrated on large, 'wilderness'-style biodiversity reserves. However, such reserves are not realistic options for densely populated regions. Here, we provide the first analyses that compare representation of biodiversity and three other ecosystem services across several contrasting conservation strategies in a human-dominated landscape (England). We show that small protected areas and protected landscapes (restrictive zoning) deliver high carbon storage and biodiversity, while existing incentive payment (agri-environment) schemes target areas that offer little advantage over other parts of England in terms of biodiversity, carbon storage and agricultural production. A fourth ecosystem service-recreation-is under-represented by all three strategies. Our findings are encouraging as they illustrate that restrictive zoning can play a major role in protecting natural capital assets in densely populated regions. However, trade-offs exist even among the four ecosystem services we considered, suggesting that a portfolio of conservation and sustainability investments will be needed to deliver both biodiversity and the other ecosystem services demanded by society.

Projected losses of global mammal and bird ecological strategies.

Species, and their ecological strategies, are disappearing. Here we use species traits to quantify the current and projected future ecological strategy diversity for 15,484 land mammals and birds. We reveal an ecological strategy surface, structured by life-history (fast-slow) and body mass (small-large) as one major axis, and diet (invertivore-herbivore) and habitat breadth (generalist-specialist) as the other. We also find that of all possible trait combinations, only 9% are currently realized. Based on species' extinction probabilities, we predict this limited set of viable strategies will shrink further over the next 100 years, shifting the mammal and bird species pool towards small, fast-lived, highly fecund, insect-eating, generalists. In fact, our results show that this projected decline in ecological strategy diversity is much greater than if species were simply lost at random. Thus, halting the disproportionate loss of ecological strategies associated with highly threatened animals represents a key challenge for conservation.

Incorporating fine-scale environmental heterogeneity into broad-extent models.

A key aim of ecology is to understand the drivers of ecological patterns, so that we can accurately predict the effects of global environmental change. However, in many cases, predictors are measured at a finer resolution than the ecological response. We therefore require data aggregation methods that avoid loss of information on fine-grain heterogeneity.We present a data aggregation method that, unlike current approaches, reduces the loss of information on fine-grain spatial structure in environmental heterogeneity for use with coarse-grain ecological datasets. Our method contains three steps: (a) define analysis scales (predictor grain, response grain, scale-of-effect); (b) use a moving window to calculate a measure of variability in environment (predictor grain) at the process-relevant scale (scale-of-effect); and (c) aggregate the moving window calculations to the coarsest resolution (response grain). We show the theoretical basis for our method using simulated landscapes and the practical utility with a case study. Our method is available as the grainchanger r package.The simulations show that information about spatial structure is captured that would have been lost using a direct aggregation approach, and that our method is particularly useful in landscapes with spatial autocorrelation in the environmental predictor variable (e.g. fragmented landscapes) and when the scale-of-effect is small relative to the response grain. We use our data aggregation method to find the appropriate scale-of-effect of land cover diversity on Eurasian jay Garrulus glandarius abundance in the UK. We then model the interactive effect of land cover heterogeneity and temperature on G. glandarius abundance. Our method enables us quantify this interaction despite the different scales at which these factors influence G. glandarius abundance.Our data aggregation method allows us to integrate variables that act at varying scales into one model with limited loss of information, which has wide applicability for spatial analyses beyond the specific ecological context considered here. Key ecological applications include being able to estimate the interactive effect of drivers that vary at different scales (such as climate and land cover), and to systematically examine the scale dependence of the effects of environmental heterogeneity in combination with the effects of climate change on biodiversity.