Multi-annual fluxes of carbon dioxide from an intensively cultivated temperate peatland

East Anglia contains the largest continuous area of lowland fen peatlands in the United Kingdom (UK) which store vast quantities of terrestrial carbon (C) that have accrued over millennia. These long term C stores have largely been drained and converted for agricultural land use over the last 400 years due to their high agricultural production potential. Initial drainage of these peatlands leads to surface lowering and peat wastage. Prolonged exposure of carbon dense peat soils to oxygen through continued agricultural management results in sustained losses of carbon dioxide (CO2) to the atmosphere. An increasing population in the UK has the potential to put further stress on these productive but rapidly diminishing Grade 1 agricultural land. Improving our understanding of land management impacts on CO2 emissions from these soils is crucial to improving their longevity as an important store of C and as an economic resource. Our measurements at an intensively cultivated lowland peatland in Norfolk, UK, are the first multi-annual record using the micrometeorological eddy covariance (EC) technique to measure CO2 fluxes associated with the production of horticultural salad crops. Three full years of flux measurements over leek (2013), lettuce (2014) and celery (2015) cropping systems found that the site was a net annual source of CO2 with a net ecosystem exchange (NEE) of 6.59, 7.84 and 7.71 t C-CO2 ha-1 a-1 respectively. The leek crop, with its longer growing period, had a lower annual NEE due to its long growth period from early spring through to late autumn, whereas the shorter growing periods of lettuce and celery meant their peak growth (CO2 uptake, Gross Primary Productivity, GPP) took place during early/mid-summer with post-harvest weeds exploiting the later growing season but exhibited lower CO2 assimilation than the leek crop. Periods of high CO2 emissions from the soil to the atmosphere were measured during mechanical disruptions to the soils at the site, namely during and after ploughing prior to crop establishment, and following post-harvest disking. The duration of the post-ploughing period prior to planting of the crop varied from 2 weeks to 2 months; similarly disking did not always take place directly after harvest, causing significant differences in seasonal NEE patterns. Further notable differences in net CO2 fluxes as they relate to agricultural management practises will be discussed, including an account of the lateral C imports and exports occurring during crop planting and harvest

New look at understanding hydrological role of forest

The article is concerned with the discussion of the reasons for the contradictions existing in the assessment of the hydrological role of forests. The authors believe that the accumulation of new information related to seemingly well-studied processes and phenomena necessitates revisions of traditional views and leads to new knowledge of the hydrological role of forests. Various conceptual approaches to assessing the hydrological role of forests in different geographic conditions are considered. System analysis of the materials obtained by the authors and literature data made it possible to identify the features of the hydrological cycle depending on the structure of forests and climatic conditions. The data of 460 snow surveys in the period of maximum snow reserves in 212 forest stands growing in different climatic and ecological conditions were used. The comparison of the features of snow moisture balance of the forest and treeless ecosystems in different climatic conditions contributed to understanding the reasons for the contradictory assessments of the hydrological role of forests. The authors showed that in the conditions of mild and warm winters, forests are more powerful evaporators of snow moisture than treeless sites and in conditions of severe winters with frequent snowstorms, they are the accumulators of snow moisture and sources of river flow. The paper presents a conceptual model describing the mechanisms of water cycle in the forests of the boreal zone, which determine the features of the influence of forest ecosystems on the river runoff depending on the geophysical background

The european water framework directive facing current challenges: recommendations for a more efficient biological assessment of inland surface waters

High quality water is vital for human life, and ensuring its availability is a basic requirement and a major societal aim. The Water Framework Directive (WFD; 2000/60/EC) is a key piece of legislation for the protection and sustainable use of water in the European Union. In this work we briefly review the WFD directive and the current status of European inland surface waters. Additionally, we summarize major challenges and threats for the biological assessment of inland surface waters under climate change effects and invasion by alien species, and highlight the emerging tools and approaches that might help improve biological assessments, including molecular indices based on environmental DNA (eDNA), to new data from the Earth Observation programmes, and data-sharing platforms. Finally, we present recommendations to improve monitoring systems and assessments in the context of the WFD. Developments in this field may increase the likelihood of assuring high quality water for societyFRESHING Project funded by the Portuguese Foundation for Science and Technology (FCT) and COMPETE (PTDC/AAG-MAA/ 2261/2014 – POCI-01-0145-FEDER-356 016824). AFF, AGR, and JPR were supported by FRESHING. FMSM was supported by FCT grant SFRH/BD/104703/2014. MJF was supported by the strategic project UID/MAR/04292/2013 granted to MAR

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100

Connecting Earth observation to high-throughput biodiversity data

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services

Connecting Earth Observation to High-Throughput Biodiversity Data

There is much interest in using Earth Observation (EO) technology to track biodiversity, ecosystem functions, and ecosystem services, understandable given the fast pace of biodiversity loss. However, because most biodiversity is invisible to EO, EO-based indicators could be misleading, which can reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing, and modern ecological modelling to extract much more of the information available in EO data. This approach is achievable now, 62 offering efficient and near-real time monitoring of management impacts on biodiversity and its functions and services

Call for Papers: DIT 2013 - dr.Juraj Plenković

Abstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50?×?50?m, equivalent to 0.25?ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r?=?0.93) and a low uncertainty (RMSE?=?3?m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%

Religious affiliation modulates weekly cycles of cropland burning in Sub-Saharan Africa

Research ArticleVegetation burning is a common land management practice in Africa, where fire is used for hunting, livestock husbandry, pest control, food gathering, cropland fertilization, and wildfire prevention. Given such strong anthropogenic control of fire, we tested the hypotheses that fire activity displays weekly cycles, and that the week day with the fewest fires depends on regionally predominant religious affiliation.We also analyzed the effect of land use (anthrome) on weekly fire cycle significance. Fire density (fire counts.km-2) observed per week day in each region was modeled using a negative binomial regression model, with fire counts as response variable, region area as offset and a structured random effect to account for spatial dependence. Anthrome (settled, cropland, natural, rangeland), religion (Christian, Muslim, mixed) week day, and their 2-way and 3-way interactions were used as independent variables. Models were also built separately for each anthrome, relating regional fire density with week day and religious affiliation. Analysis revealed a significant interaction between religion and week day, i.e. regions with different religious affiliation (Christian, Muslim) display distinct weekly cycles of burning. However, the religion vs. week day interaction only is significant for croplands, i.e. fire activity in African croplands is significantly lower on Sunday in Christian regions and on Friday in Muslim regions. Magnitude of fire activity does not differ significantly among week days in rangelands and in natural areas, where fire use is under less strict control than in croplands. These findings can contribute towards improved specification of ignition patterns in regional/global vegetation fire models, and may lead to more accurate meteorological and chemical weather forecastinginfo:eu-repo/semantics/publishedVersio

Conservation of pattern as a tool for inference on spatial snapshots in ecological data

As climate change and other anthropogenic factors increase the uncertainty of vegetation ecosystem persistence, the ability to rapidly assess their dynamics is paramount. Vegetation and sessile communities form a variety of striking regular spatial patterns such as stripes, spots and labyrinths, that have been used as indicators of ecosystem current state, through qualitative analysis of simple models. Here we describe a new method for rigorous quantitative estimation of biological parameters from a single spatial snapshot. We formulate a synthetic likelihood through consideration of the expected change in the correlation structure of the spatial pattern. This then allows Bayesian inference to be performed on the model parameters, which includes providing parameter uncertainty. The method was validated against simulated data and then applied to real data in the form of aerial photographs of seagrass banding. The inferred parameters were found to be able to reproduce similar patterns to those observed and able to detect strength of spatial competition, competition-induced mortality and the local range of reproduction. This technique points to a way of performing rapid inference of spatial competition and ecological stability from a single spatial snapshots of sessile communities