Predicting the past distribution of species climatic niches

Predicting past distributions of species climatic niches, hindcasting, by using climate envelope models (CEMs) is emerging as an exciting research area. CEMs are used to examine veiled evolutionary questions about extinctions, locations of past refugia and migration pathways, or to propose hypotheses concerning the past population structure of species in phylogeographical studies. CEMs are sensitive to theoretical assumptions, to model classes and to projections in non-analogous climates, among other issues. Studies hindcasting the climatic niches of species often make reference to these limitations. However, to obtain strong scientific inferences, we must not only be aware of these potential limitations but we must also overcome them. Here, I review the literature on hindcasting CEMs. I discuss the theoretical assumptions behind niche modelling, i.e. the stability of climatic niches through time and the equilibrium of species with climate. I also summarize a set of 'recommended practices' to improve hindcasting. The studies reviewed: (1) rarely test the theoretical assumptions behind niche modelling such as the stability of species climatic niches through time and the equilibrium of species with climate; (2) they only use one model class (72% of the studies) and one palaeoclimatic reconstruction (62.5%) to calibrate their models; (3) they do not check for the occurrence of non-analogous climates (97%); and (4) they do not use independent data to validate the models (72%). Ignoring the theoretical assumptions behind niche modelling and using inadequate methods for hindcasting CEMs may well entail a cascade of errors and naïve ecological and evolutionary inferences. We should also push integrative research lines linking macroecology, physiology, population biology, palaeontology, evolutionary biology and CEMs for a better understanding of niche dynamics across space and time.     

The smaller fruit tortrix, Grapholita lobarzewskii: predicting the phenology of adult emergence

The smaller fruit tortrix, Grapholita lobarzewskii Nowicki, has become a major pest in the recent past in apple orchards north of the Alps. Nevertheless little is known about the biology and the behaviour of this species. The effect of temperature on post-diapause development and survival was studied under controlled conditions in order to establish a basis for the forecasting of adult emergence. Survival was found to be highest at 17 °C and lowest at 11.8 °C. The sex ratio did not differ significantly from 1:1. Based on a linear relationship between temperature and developmental rates, thermal thresholds of 9.6 °C and 9.8 °C were determined for females and males respectively. The thermal constants, i.e., the mean developmental times in physiological time units, were found to be 342 day-degrees for females and 317 day-degrees for males, suggesting a slight protandry. Based on the mean and the variance of the developmental times, a simple temperature-driven phenology model was built using a time-varying distributed delay. The model was validated by visually comparing the predictions with independent observations on adult emergence, and by calculating the temporal deviations of the predictions. In 4 out of 5 years the mean error was less than 3 days. The model was therefore found to give reliable forecasts of the emergence of G. lobarzewskii and can be used to determine the optimal time for the exposure of pheromone traps, the application of pheromone dispensers for mating disruption, and for the timing of insecticide applications.     

Epidemiology of Leptosphaeria maculans in relation to forecasting stem canker severity on winter oilseed rape in the UK

In the UK, ascospores of Leptosphaeria maculans first infect leaves of oilseed rape in the autumn to cause phoma leaf spots, from which the fungus can grow to cause stem cankers in the spring. Yield losses due to early senescence and lodging result if the stem cankers become severe before harvest. The risk of severe stem canker epidemics needs to be forecast in the autumn when the pathogen is still in the leaves, since early infections cause the greatest yield losses and fungicides have limited curative activity. Currently, the most effective way to forecast severe stem canker is to monitor the onset of phoma leaf spotting in winter oilseed rape crops, although this does not allow much time in which to apply a fungicide. Early warnings of risks of severe stem canker epidemics could be provided at the beginning of the season through regional forecasts based on disease survey and weather data, with options for input of crop-specific information and for updating forecasts during the winter. The accuracy of such forecasts could be improved by including factors relating to the maturation of ascospores in pseudothecia, the release of ascospores and the occurrence of infection conditions, as they affect the onset, intensity and duration of the phoma leaf spotting phase. Accurate forecasting of severe stem canker epidemics can improve disease control and optimise fungicide use.     

The improvement of the algorithm for order parameter calculation (S2) from molecular dynamics simulation using the correlation motion function

The generalized order parameter, S2, calculated from MD simulation trajectory using time-dependent internal Correlation Motion Function (CMF) agrees well with NMR derived S2 processed with the extended model-free analysis approach. However, the former lies considerably lower comparing to simple model-free derived data from NMR experiments. In the present study we analyze possible reasons of such disagreement. In the general case we propose to use preexponential factors from expression for internal CMF rather than ordinary S2 values. Particularly, in case of the simple model-free S(2) experimental values we suggest comparing them with S2(eff)=1+S2-Sf2 computed from MD simulation data. We show that the S2(eff) values are in a good agreement with NMR derived S2 values obtained using the simple model-free analysis.     

Ecological forecasting under climate change: the case of Baltic cod

Good decision making for fisheries and marine ecosystems requires a capacity to anticipate the consequences of management under different scenarios of climate change. The necessary ecological forecasting calls for ecosystem-based models capable of integrating multiple drivers across trophic levels and properly including uncertainty. The methodology presented here assesses the combined impacts of climate and fishing on marine food-web dynamics and provides estimates of the confidence envelope of the forecasts. It is applied to cod (Gadus morhua) in the Baltic Sea, which is vulnerable to climate-related decline in salinity owing to both direct and indirect effects (i.e. through species interactions) on early-life survival. A stochastic food web-model driven by regional climate scenarios is used to produce quantitative forecasts of cod dynamics in the twenty-first century. The forecasts show how exploitation would have to be adjusted in order to achieve sustainable management under different climate scenarios.     

Panel semiparametric quantile regression neural network for electricity consumption forecasting

Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption forecasting procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By embedding penalized quantile regression with least absolute shrinkage and selection operator (LASSO), ridge regression and backpropagation, PSQRNN keeps the flexibility of nonparametric models and the interpretability of parametric models simultaneously. The prediction accuracy is evaluated based on China's electricity consumption data set, and the results indicate that PSQRNN performs better compared with three benchmark methods including BP neural network (BP), Support Vector Machine (SVM) and Quantile Regression Neural Network (QRNN).     

Ecological forecasts to inform near‐term management of threats to biodiversity

Ecosystems are being altered by rapid and interacting changes in natural processes and anthropogenic threats to biodiversity. Uncertainty in historical, current and future effectiveness of actions hampers decisions about how to mitigate changes to prevent biodiversity loss and species extinctions. Research in resource management, agriculture and health indicates that forecasts predicting the effects of near‐term or seasonal environmental conditions on management greatly improve outcomes. Such forecasts help resolve uncertainties about when and how to operationalize management. We reviewed the scientific literature on environmental management to investigate whether near‐term forecasts are developed to inform biodiversity decisions in Australia, a nation with one of the highest recent extinction rates across the globe. We found that forecasts focused on economic objectives (e.g. fisheries management) predict on significantly shorter timelines and answer a broader range of management questions than forecasts focused on biodiversity conservation. We then evaluated scientific literature on the effectiveness of 484 actions to manage seven major terrestrial threats in Australia, to identify opportunities for near‐term forecasts to inform operational conservation decisions. Depending on the action, between 30% and 80% threat management operations experienced near‐term weather impacts on outcomes before, during or after management. Disease control, species translocation/reintroduction and habitat restoration actions were most frequently impacted, and negative impacts such as increased species mortality and reduced recruitment were more likely than positive impacts. Drought or dry conditions, and rainfall, were the most frequently reported weather impacts, indicating that near‐term forecasts predicting the effects of low or excessive rainfall on management outcomes are likely to have the greatest benefits. Across the world, many regions are, like Australia, becoming warmer and drier, or experiencing more extreme rainfall events. Informing conservation decisions with near‐term and seasonal ecological forecasting will be critical to harness uncertainties and lower the risk of threat management failure under global change.     

Bridging parametric and nonparametric measures of species interactions unveils new insights of non-equilibrium dynamics

A central theme in ecological research is to understand how species interactions contribute to community dynamics. Species interactions are the basis of parametric (model-driven) and nonparametric (model-free) approaches in theoretical and empirical work. However, despite their different interpretations across these approaches, these measures have occasionally been used interchangeably, limiting our opportunity to use their differences to gain new insights about ecological systems. Here, we revisit two of the most used measures across these approaches: species interactions measured as constant direct effects (typically used in parametric approaches) and local aggregated effects (typically used in nonparametric approaches). We show two fundamental properties of species interactions that cannot be revealed without bridging these definitions. First, we show that the local aggregated intraspecific effect summarizes all potential pathways through which one species impacts itself, which are likely to be negative even without any constant direct self-regulation mechanism. This property has implications for the long-held debate on how communities can be stabilized when little evidence of self-regulation has been found among higher-trophic species. Second, we show that a local aggregated interspecific effect between two species is correlated with the constant direct interspecific effect if and only if the population dynamics do not have any higher-order direct effects. This other property provides a rigorous methodology to detect direct higher-order effects in the field and experimental data. Overall, our findings illustrate a practical route to gain further insights about non-equilibrium ecological dynamics and species interactions.     

Stage‐specific probabilistic phenology model of Cydia pomonella (Lepidoptera: Tortricidae) using laboratory maximum likelihood parameter estimates

In this study, a probabilistic degree‐day phenology model has been developed for the codling moth, Cydia pomonella, and calibrated using data from laboratory growth studies. The model is further used to predict the succession and overlapping of certain biological events of C. pomonella in probabilistic‐physiological time scale in northern Greece fruit orchards. The model satisfactorily predicts the stage‐specific pest population dynamics, including egg laying and hatching, the occurrence of larvae and pupae stages and the emergence of adults. According to the model projections for the adult flights, there is a very high probability, p = 0.999, of observing adults of the first flight generation until 333 degree‐days (DD), but a very low probability of finding adults of the second flight generation. Moreover, at 575 DD, the probability of finding an individual to lay eggs is p = 0.15. However, there is nearly the same probability of egg hatch, p = 0.36, and larval completion p = 0.313, while at the same time, the probability of pupal completion is very low, p = 0.001. The above model predictions were validated using field data for the adult stage emergence as well as for the percentage of larval damage providing satisfactory results considering that larval emergence prediction was close to actual fruit damage observed in field. This information is very important considering that IPM programs rely on the use of biorational compounds, such as IGRs and bio‐toxins which are stage selective and often have a shorter residual activity than the preceding broad‐spectrum insecticides.     

Assessing future energy and transport systems: the case of fuel cells

Goal, Scope and Background  Assessing future energy and transport systems is of major importance for providing timely information for decision makers. In the discussion of technology options, fuel cells are often portrayed as attractive options for power plants and automotive applications. However, when analysing these systems, the LCA analyst is confronted with methodological problems, particularly with data gaps and the requirement of an anticipation of future developments. This series of two papers aims at providing a methodological framework for assessing future energy and transport systems (Part 1) and applies this to the two major application areas of fuel cells (Part 2). Methods  To allow the LCA of future energy and transport systems forecasting tools like, amongst others, cost estimation methods and process simulation of systems are investigated with respect to the applicability in LCAs of future systems (Part 1). The manufacturing process of an SOFC stack is used as an illustration for the forecasting procedure. In Part 2, detailed LCAs of fuel cell power plants and power trains are carried out including fuel (hydrogen, methanol, gasoline, diesel and natural gas) and energy converter production. To compare it with competing technologies, internal combustion engines (automotive applications) and reciprocating engines, gas turbines and combined cycle plants (stationary applications) are analysed as well. Results and Discussion  Principally, the investigated forecasting methods are suitable for future energy system assessment. The selection of the best method depends on different factors such as required ressources, quality of the results and flexibility. In particular, the time horizon of the investigation determines which forecasting tool may be applied. Environmentally relevant process steps exhibiting a significant time dependency shall always be investigated using different independent forecasting tools to ensure stability of the results. The results of the LCA (Part 2) underline that principally, fuel cells offer advantages in the impact categories which are typically dominated by pollutant emissions, such as acidification and eutrophication, whereas for global warming and primary energy demand, the situation depends on a set of parameters such as driving cycle and fuel economy ratio in mobile applica-tions and thermal/total efficiencies in stationary applications. For the latter impact categories, the choice of the primary en-ergy carrier for fuel production (renewable or fossil) dominates the impact reduction. With increasing efficiency and improving emission performance of the conventional systems, the competition regarding all impact categories in both mobile and stationary applications is getting even stronger. The production of the fuel cell system is of low overall significance in stationary applications, whereas in automotive applications, the production of the fuel cell power train and required materials leads to increased impacts compared to internal combustion engines and thus reduces the achievable environmental impact reduction. Recommendations and Perspectives  The rapid technological and energy economic development will bring further advances for both fuel cells and conventional energy converters. Therefore, LCAs at such an early stage of the market development can only be considered preliminary. It is an essential requirement to accompany the ongoing research and development with iterative LCAs, constantly pointing at environmental hot spots and bottlenecks.