Value of weather observations for reduction of forest fire impact on population

In this paper we investigate how improvements in the weather observation systems help to reduce forest fires impact on population by targeting and monitoring places where ripe fires are likely to occur. For the purposes of population impact assessment we suggest a relevant index. In our model the air patrolling schedule is determined by the Nesterov index, which is calculated from observed weather data sets at different spatial resolutions. The reduction of fire impact on population, associated with utilization of finer grid, indicates the benefits of more precise weather observations. We also explore the sensitivity of the forest fires model with respect to the quality of input data while taking into account the multitude of sources providing weather observations. Our model shows that approximately 90% of the feasible reduction of fire impact on population can be achieved by refining weather observations in 30% of the area of interest

Image Restoration from Multiple Sources

This paper proposes a new method of image restoration. The proposed method allows to combine information from several sources, taking the perceived credibility of each into account. It is applicable to both ordinal (e.g., gray level image) and non-ordinal (e.g., classified forest map) categorized images. The accuracy checks have shown the method to be robust with respect to the prior information and the accuracy of the sources. Two application examples are provided

Disentangling niche theory and beta diversity change

Beta diversity describes the differences in species composition among communities. Changes in beta diversity over time are thought to be due to selection based on species’ niche characteristics. For example, theory predicts that selection that favors habitat specialists will increase beta diversity. In practice, ecologists struggle to predict how beta diversity changes. To remedy this problem, we propose a novel solution that formally measures selection’s effects on beta diversity. Using the Price equation, we show how change in beta diversity over time can be partitioned into fundamental mechanisms including selection among species, variable selection among communities, drift, and immigration. A key finding of our approach is that a species’ short-term impact on beta diversity cannot be predicted using information on its long-term environmental requirements (i.e., its niche). We illustrate how our approach can be used to partition causes of diversity change in a montane tropical forest before and after an intense hurricane. Previous work in this system highlighted the resistance of habitat specialists and the recruitment of light-demanding species but was unable to quantify the importance of these effects on beta diversity. Using our approach, we show that changes in beta diversity were consistent with ecological drift. We use these results to highlight the opportunities presented by a synthesis of beta diversity and formal models of selection

Improved Estimates of Biomass Expansion Factors for Russian Forests

Biomass structure is an important feature of terrestrial vegetation. The parameters of forest biomass structure are important for forest monitoring, biomass modelling and the optimal utilization and management of forests. In this paper, we used the most comprehensive database of sample plots available to build a set of multi-dimensional regression models that describe the proportion of different live biomass fractions (i.e., the stem, branches, foliage, roots) of forest stands as a function of average stand age, density (relative stocking) and site quality for forests of the major tree species of northern Eurasia. Bootstrapping was used to determine the accuracy of the estimates and also provides the associated uncertainties in these estimates. The species-specific mean percentage errors were then calculated between the sample plot data and the model estimates, resulting in overall relative errors in the regression model of −0.6%, −1.0% and 11.6% for biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF), and root-to-shoot ratio respectively. The equations were then applied to data obtained from the Russian State Forest Register (SFR) and a map of forest cover to produce spatially distributed estimators of biomass conversion and expansion factors and root-to-shoot ratios for Russian forests. The equations and the resulting maps can be used to convert growing stock volume to the components of both above-ground and below-ground live biomass. The new live biomass conversion factors can be used in different applications, in particular to substitute those that are currently used by Russia in national reporting to the UNFCCC (United Nations Framework Convention on Climate Change) and the FAO FRA (Food and Agriculture Organization’s Forest Resource Assessment), among others

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs

Disentangling niche theory and beta diversity change

Beta diversity describes the differences in species composition among communities. Changes in beta diversity over time are thought to be due to selection based on species’ niche characteristics. For example, theory predicts that selection that favours habitat specialists will increase beta diversity. In practice, ecologists struggle to predict how beta diversity changes. To remedy this problem, we propose a novel solution that formally measures selection’s effects on beta diversity. Using the Price equation, we show how change in beta diversity over time can be partitioned into fundamental mechanisms including selection among species, variable selection among communities, drift, and immigration. A key finding of our approach is that a species’ short-term impact on beta diversity cannot be predicted using information on its long-term environmental requirements (i.e. its niche). We illustrate how our approach can be used to partition causes of diversity change in a montane tropical forest before and after an intense hurricane. Previous work in this system highlighted the resistance of habitat specialists and the recruitment of light-demanding species but was unable to quantify the importance of these effects on beta diversity. Using our approach, we show that changes in beta diversity were consistent with ecological drift. We use these results to highlight the opportunities presented by a synthesis of beta diversity and formal models of selection

Uncertainty in soil data can outweigh climate impact signals in crop yield simulations

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations

Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach

Citizen science has become an increasingly popular approach to scientific data collection, where classification tasks involving visual interpretation of images is one prominent area of application, e.g., to support the production of land cover and land-use maps. Achieving a minimum accuracy in these classification tasks at a minimum cost is the subject of this study. A Bayesian approach provides an intuitive and reasonably straightforward solution to achieve this objective. However, its application requires additional information, such as the relative frequency of the classes and the accuracy of each user. While the former is often available, the latter requires additional data collection. In this paper, we present a two-stage approach to gathering this additional information. We demonstrate its application using a hypothetical two-class example and then apply it to an actual crowdsourced dataset with five classes, which was taken from a previous Geo-Wiki crowdsourcing campaign on identifying the size of agricultural fields from very high-resolution satellite imagery. We also attach the R code for the implementation of the newly presented approach

Respiration of Russian soils: climatic drivers and response to climate change

Soil respiration is one of the major ecosystem carbon fluxes and has a strong relationship with climate. We quantified this dependence for the Russian territory based on coupling climate data and in-situ soil respiration measurements compiled into a database from the literature using regression and random forest models. The analysis showed that soil properties are a strong factor that mediates the climate effect on Rs. The vegetation class determines the contribution of the autotrophic respiration to the total Rs flux. The heterotrophic soil respiration efflux of Russia was estimated to be 3.2 Pg C yr-1 or 190 g C m-2 yr-1, which is 9-20% higher than most previously reported estimates. According to our modeling, heterotrophic soil respiration is expected to rise by 12% on average by 2050 according to the RCP2.6 climate scenario and at 10% based on RCP6. The total for Russia may reach 3.5 Pg C yr-1 by 2050. By the end of the century heterotrophic respiration may reach 3.6 Pg C yr-1 (+13%) and 4.3 Pg C yr-1 (+34%) based on RCP2.6 and RCP6, respectively. In order to understand to what extent the lack of information on disturbances impact contributes to uncertainty of our model, we analyzed a few available publications and expert estimates. Taking into account the specifics of Russian forest management and regional disturbance regimes, we have found that for the entire territory of Russia, the disturbances are responsible for an increase in heterotrophic soil respiration by less than 2%

Evaluating sources of variability in inflorescence number, flower number and the progression of flowering in Sauvignon blanc using a Bayesian modelling framework

The time of flowering is key to understanding the development of grapevines. Flowering coincides with inflorescence initiation and fruit set, important determinants of yield. This research aimed to determine between and within-vine variability in 4-cane-pruned Sauvignon blanc inflorescence number per shoot, number of flowers per inflorescence and flowering progression using an objective method of assessing flowering via image capture and statistical analysis using a Bayesian modelling framework. The inflorescence number and number of flowers per inflorescence were measured by taking images over the flowering period. Flowering progression was assessed by counting open and closed flowers for each image over two seasons. An ordinal multinomial generalised linear mixed-effects model (GLMM) was fitted for inflorescence number, a Poisson GLMM for flower counts and a binomial GLMM for flowering progression. All the models were fitted and interpreted within a Bayesian modelling framework. Shoots arising from cane node one had lower numbers of inflorescences compared to those at nodes 3, 5 and 7, which were similar. The number of flowers per inflorescence was greater for basal inflorescences on a shoot than apical ones. Flowering was earlier, by two weeks, and faster in 2017/18 when compared to 2018/19 reflecting seasonal temperature differences. The time and duration of flowering varied at each inflorescence position along the cane. While basal inflorescences flowered later and apical earlier at lower insertion points on the shoot, the variability in flowering at each position on the vine dominated the date and duration of flowering. This is the first study using a Bayesian modelling framework to assess variability inflorescence presence and flower number, as well as flowering progression via objective quantification of open and closed flower counts rather than the more subjective method of visual estimation in the field or via cuttings. Although flower number differed for apical and basal bunches, little difference in timing and progression of flowering by these categories was observed. The node insertion point along a shoot was more important. Overall, the results indicate individual inflorescence variation and season are the key factors driving flowering variability and are most likely to impact fruit set and yield