A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.

Provision of ecological infrastructures to increase pollinators and other beneficial organisms in rainfed crops in Central Spain

In sustainable intensive agriculture, the biodiversity of monoculture fields can be increased by managing the field margins to provide ecological infrastructures that serve as refuges and resources for beneficial organisms (pollinators and natural enemies). In the present work we summarize two years of field trials following the goal to increase biodiversity of beneficial fauna in a barley field in Central Spain by sowing different herbaceous mixtures in the field margins. The presence of arthropods visiting flowers on plots sown with different types of seed mixtures and unsown natural flora (control plot) was compared by visual sampling every week between April and June. The results showed that a combination of herbaceous big-size seeds was the most successful mixture emerging under our experimental conditions and achieved a higher number of visits of beneficial arthropods than the unsown natural vegetation.

Indicators of sustainability of agriculture and livestock in Spain - Indicadores de sostenibilidad de la agricultura y ganadería española

Sustainability is an adjective used to characterize agriculture according to the degree of fulfillment of goals. Those goals are related to agro-ecological, environmental and socio-economic dimensions. Sustainability is a dynamic and temporal character. In absolute terms there is not an ending value because it changes as its dimensions make it. Spain is one of the main agricultural countries of the European Union both in terms of crop land and value of productions. The object of this study is to present a methodology of sustainability account to be incorporated into national statistical and to assess their performance in the course of the years. For that reason the data sources used have been the statistics of the Department of Agriculture and from others database. We presented a set of indicators of sustainability and its evaluation in a time series of at least 30 years. The trend analysis offers the evolution of the numerical values of the indicators in terms of efficiency, physical units used for a unit of product or its value in euros. The analyzed crops have been: wheat, barley, maize, sunflower, sugar beet, wine grape, olive oil, citrus, melon and tomato. Physical indicators were: land, water, energy, erosion, soil organic matter, and carbon balance; socio-economic indicators were: agricultural final production, prices, income, employment and use of fertilizers. In general, all crops increased their productive efficiency, higher in irrigated than on dry land. Spanish agricultural carbon sequestration capacity has multiplied by five in the last seventy years, as a result of the increase in the productivity of crops, in terms of total biomass and the modification of the soil management techniques. Livestock sector presents data of pork, broilers and laying hen. Those showed an improvement in efficiency and economic indicators. Overall we can say that Spanish agriculture and livestock subsector have a tendency towards sustainability, being its main threats extreme meteorological factors and the instability of todays markets.

Nitrous oxide emissions from European agriculture - an analysis of variability and drivers of emissions from field experiments

Nitrous oxide emissions from a network of agricultural experiments in Europe were used to explore the relative importance of site and management controls of emissions. At each site, a selection of management interventions were compared within replicated experimental designs in plot-based experiments. Arable experiments were conducted at Beano in Italy, El Encin in Spain, Foulum in Denmark, Logarden in Sweden, Maulde in Belgium CE1, Paulinenaue in Germany, and Tulloch in the UK. Grassland experiments were conducted at Crichton, Nafferton and Peaknaze in the UK, Godollo in Hungary, Rzecin in Poland, Zarnekow in Germany and Theix in France. Nitrous oxide emissions were measured at each site over a period of at least two years using static chambers. Emissions varied widely between sites and as a result of manipulation treatments. Average site emissions (throughout the study period) varied between 0.04 and 21.21 kg N2O-N ha−1yr−1, with the largest fluxes and variability associated with the grassland sites. Total nitrogen addition was found to be the single most important deter- minant of emissions, accounting for 15 % of the variance (using linear regression) in the data from the arable sites (p<0.0001), and 77 % in the grassland sites. The annual emissions from arable sites were significantly greater than those that would be predicted by IPCC default emission fac- tors. Variability of N2O emissions within sites that occurred as a result of manipulation treatments was greater than that resulting from site-to-site and year-to-year variation, highlighting the importance of management interventions in contributing to greenhouse gas mitigation

Climate Change, Water Scarcity in Agriculture and The Country-Level Economic Impacts. A Multimarket Analysis

Climate Change, Water Scarcity in Agriculture and the Country-Level Economic Impacts. A Multimarket Analysis. Abstract: Agriculture could be one of the most vulnerable economic sectors to the impacts of climate change in the coming decades. Considering the critical role that water plays for agricultural production, any shock in water availability will have great implications for agricultural production, land allocation, and agricultural prices. In this paper, an Agricultural Multimarket model is developed to analyze climate change impacts in developing countries, accounting for the uncertainty associated with the impacts of climate change. The model has a structure flexible enough to represent local conditions, resource availability, and market conditions. The results suggest different economic consequences of climate change depending on the specific activity, with many distributional effects across regions