Impact of protein concentrate coproducts on net land requirement for European biofuel production

A methodology is proposed for calculating the net land area requirement for European biofuels, accounting for the substitution impact of animal feed protein coproducts such as dried distillers grains and solubles (DDGS) and rape meal. For example, when bioethanol is produced from cereal grain starch, grain protein is preserved in the DDGS coproduct. Each tonne of wheat DDGS has the potential to replace 0.59 tonnes of soy meal and 0.39 tonnes of cereals in EU animal feed, and the land area required for soy and cereal feed production offsets much of the land requirement for wheat bioethanol feedstock. While the land area needed for bioethanol from feed wheat in North West Europe is 0.40 ha t^?1, the net requirement after accounting for coproducts is just 0.03ha t^?1 of bioethanol produced, 6% of the gross land requirement. Calculated in this way, the net land area required to produce biofuel from EU cereal, rapeseed and sugar beet crops is much lower than the gross land requirement, and from cereal and sugar beet crops is less than the land requirement of biofuel from oil palm and sugar cane.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Will climate change promote future invasions?

Biological invasion is increasingly recognized as one of the greatest threats to biodiversity. Using ensemble forecasts from species distribution models to project future suitable areas of the 100 of the world's worst invasive species defined by the International Union for the Conservation of Nature, we show that both climate and land use changes will likely cause drastic species range shifts. Looking at potential spatial aggregation of invasive species, we identify three future hotspots of invasion in Europe, northeastern North America, and Oceania. We also emphasize that some regions could lose a significant number of invasive alien species, creating opportunities for ecosystem restoration. From the list of 100, scenarios of potential range distributions show a consistent shrinking for invasive amphibians and birds, while for aquatic and terrestrial invertebrates distributions are projected to substantially increase in most cases. Given the harmful impacts these invasive species currently have on ecosystems, these species will likely dramatically influence the future of biodiversity.     

Terrestrial models and global change: challenges for the future

A wide variety of models have illustrated the potential importance of terrestrial biological feedbacks on climate and climate change; yet our ability to make precise predictions is severely limited, due to a high degree of uncertainty. In this paper, after briefly reviewing current models, we present challenges for new terrestrial models and introduce a simple mechanistic approach that may complement existing approaches.     

Carbon impacts of direct land use change in semiarid woodlands converted to biofuel plantations in India and Brazil

We present an analysis of direct land use change (dLUC) resulting from the conversion of semiarid woodlands in Brazil and India to Jatropha curcas, a perennial biofuel crop. The sites examined include prosopis woodlands, managed for woodfuel production under periodic coppicing, in southern India, and unmanaged caatinga woodlands in the Brazilian state of Minas Gerais. The jatropha plantations under consideration include pruned and unpruned stands and ranged from 2 to 4 years of age. Stocks of carbon in aboveground (AG) pools, including woody biomass, coarse debris, leaf litter, and herbaceous matter, as well as soil organic carbon (SOC) were evaluated. The jatropha plantations store 8–10 tons of carbon per hectare (t C ha^?1) in AG biomass and litter when managed with regular pruning in both India and Brazil. Unpruned trees, only examined in Brazil, store less biomass (and carbon), accumulating just 3 t C ha^?1 in AG pools. The two woodlands that were replaced with jatropha show substantial differences in carbon pools: prosopis contains ～11 t C ha^?1 in AG stocks of carbon, which was very close to the jatropha stand which replaced it. In contrast, caatinga stores ～35 t C ha^?1 in AG biomass. Moreover, no change in SOC was detected in land that was converted from Prosopis to jatropha. As a result, there is no detectable change in AG carbon stocks at the sites in South India where jatropha replaced prosopis woodlands. In contrast, large losses of AG carbon were detected in Central Brazil where jatropha replaced native caatinga woodlands. These losses represent a carbon debt that would take 10–20 years to repay.     

The impacts of Miscanthus×giganteus production on the Midwest US hydrologic cycle

Perennial grasses are being considered as candidates for biofuel feedstocks to provide an alternative energy source to fossil fuels. Miscanthus×giganteus (miscanthus), in particular, is a grass that is predicted to provide more energy per sown area than corn ethanol and reduce net carbon dioxide emissions by increasing the storage of carbon belowground. Miscanthus uses more water than Zea mays (maize), mainly as a result of a longer growing season and higher productivity. Conversion of current land use for miscanthus production will likely disrupt regional hydrologic cycles, yet the magnitude, timing, and spatial distribution of effects are unknown. Here, we show the effects of five different scenarios of miscanthus production on the simulated Midwest US hydrologic cycle. Given the same historic precipitation observations, our ecosystem model simulation results show that on an annual basis miscanthus uses more water than the ecosystems it will likely replace. The actual timing and magnitude of increased water loss to the atmosphere depends on location; however, substantial increases only occur when miscanthus fraction cover exceeds 25% in dry regions and 50% in nearly all of the Midwest. Our results delineate where large‐scale land use conversion to perennial biofuel grasses might deplete soil water resources. Given the fact that some watersheds within the Midwest already have depleted water resources, we expect our results to inform decisions on where to grow perennial grasses for biofuel use to ensure sustainability of energy and water resources, and to minimize the potential for deleterious effects to water quantity and quality.     

Predicting species distribution: offering more than simple habitat models

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.     

The ability of climate envelope models to predict the effect of climate change on species distributions

Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO₂ conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.     

Estimating demographic models for the range dynamics of plant species

Aims To better understand how demographic processes shape the range dynamics of woody plants (in this case, Proteaceae), we introduce a likelihood framework for fitting process‐based models of range dynamics to spatial abundance data. Location The fire‐prone Fynbos biome (Cape Floristic Region, South Africa). Methods Our process‐based models have a spatially explicit demographic submodel (describing dispersal, reproduction, mortality and local extinction) as well as an observation submodel (describing imperfect detection of individuals), and are constrained by species‐specific predictions of habitat distribution models and process‐based models for seed dispersal by wind. Free model parameters were varied to find parameter sets with the highest likelihood. After testing this approach with simulated data, we applied it to eight Proteaceae species that differ in breeding system (monoecy versus dioecy) and adult fire survival. We assess the importance of Allee effects and negative density dependence for range dynamics, by using the Akaike information criterion to select between alternative models fitted for the same species. Results The best model for all dioecious study species included Allee effects, whereas this was true for only one of four monoecious species. As expected, sprouters (in which adults survive fire) were estimated to have lower rates of reproduction and catastrophic population extinction than related non‐sprouters. Overcompensatory population dynamics seem important for three of four non‐sprouters. We also found good quantitative agreement between independent data and most estimates of reproduction, carrying capacity and extinction probability. Main conclusions This study shows that process‐based models can quantitatively describe how large‐scale abundance distributions arise from the movement and interaction of individuals. It stresses links between the life history, demography and range dynamics of Proteaceae: dioecious species seem more susceptible to Allee effects which reduce migration ability and increase local extinction risk, and sprouters seem to have high persistence of established populations, but their low reproduction limits habitat colonization and migration.