Gap distributions in forests reflect the spatial impact of man-made tree harvesting or naturally-induced patterns of tree death being caused by windthrow, inter-tree competition, disease or senescence. Gap sizes can vary from large (>100 m 2 ) to small (<10 m 2 ), and they may have contrasting spatial patterns, such as being aggregated or regularly distributed. However, very small gaps cannot easily be recorded with conventional aerial or satellite images, which calls for new and cost-effective methodologies of forest monitoring. Here, we used an unmanned aerial vehicle (UAV) and very high-resolution images to record the gaps in 10 temperate managed and unmanaged forests in two regions of Germany. All gaps were extracted for 1-ha study plots and subsequently analyzed with spatially-explicit statistics, such as the conventional pair correlation function (PCF), the polygon-based PCF and the mark correlation function. Gap-size frequency was dominated by small gaps of an area <5 m 2 , which were particularly frequent in unmanaged forests. We found that gap distances showed a variety of patterns. However, the polygon-based PCF was a better descriptor of patterns than the conventional PCF, because it showed randomness or aggregation for cases when the conventional PCF showed small-scale regularity; albeit, the latter was only a mathematical artifact. The mark correlation function revealed that gap areas were in half of the cases negatively correlated and in the other half independent. Negative size correlations may OPEN ACCESSRemote Sens. 2014, 6 6989 likely be the result of single-tree harvesting or of repeated gap formation, which both lead to nearby small gaps. Here, we emphasize the usefulness of UAV to record forest gaps of a very small size. These small gaps may originate from repeated gap-creating disturbances, and their spatial patterns should be monitored with spatially-explicit statistics at recurring intervals in order to further insights into forest dynamics.

Within the past few years plant functional trait analyses have been widely applied to learn more about the processes and patterns of ecosystem development in response to environmental changes. These approaches are based on the assumption that plants with similar ecologically relevant trait attributes respond to environmental changes in comparable ways. Several methods have been described on how to analyse a priori defined trait sets with respect to environment. Irrespective of the statistical methods used to contrast ecosystem responses and environmental conditions, each functional trait approach depends strongly on the initial trait set. In nearly all recent studies on functional trait analysis a test, if a trait is responsible, is applied independently from the core analysis. In the current study we present a method that extracts those traits from a wider set of traits which are optimal for describing the ecosystem response to a given environmental gradient. This was done by the use of iterative three-table ordination techniques with each possible trait combination. We further concentrated on the effect of the inclusion of too many traits in such analyses.As examples the method was applied to three long term studies on abandoned arable fields. The approach was validated by comparing the results with literature-knowledge on arable field succession. Although the trait pre-selection was only based on a statistical procedure, our method was able to identify all relevant processes of ecosystem responses. All three sites show comparable ecosystem responses; the importance of the competitive ability of plants was highlighted. We further demonstrated that the use of too many traits results in an over-fitting of the trait-environment model.The presented method of iterative RLQ-analyses is adequate to identify responding traits to environmental changes: the discovered processes of successional development of abandoned arable fields are consistent with our knowledge from the literature.

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BackgroundBy example of a region in Northern Germany (County of Uelzen), this study investigates whether climate change is likely to require adaption of agricultural practices such as irrigation in Central Europe. Due to sandy soils with low water retention capacity and occasional insufficient rainfall, irrigation is a basic condition for agricultural production in the county of Uelzen. Thus, in the framework of the comprehensive research cluster Nachhaltiges Landmanagement im Norddeutschen Tiefland (NaLaMa-nT), we investigated whether irrigation might need to be adapted to changing climatic conditions. To this end, results from regionalised climate change modelling were coupled with soil- and crop-specific evapotranspiration models to calculate potential amounts of irrigation to prevent crop failures. Three different runs of the climate change scenario RCP 8.5 were used for the time period until 2070.ResultsThe results show that the extent of probable necessary irrigation will likely increase in the future. For the scenario run with the highest temperature rise, the results suggest that the amount of ground water presently allowed to be extracted for irrigation might not be sufficient in the future to retain common agricultural pattern.ConclusionsThe investigation at hand exemplifies data requirements and methods to estimate irrigation needs under climate change conditions. Restriction of ground water withdrawal by German environmental regulation may require an adaptation of crop selection and alterations in agricultural practice also in regions with comparable conditions.

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