Insekten stellen die artenreichste Klasse des Tierreichs dar, wobei viele der Arten bedroht sind. Das liegt neben dem Klimawandel vor allem an der sich in den letzten Jahrzehnten stark verändernden landwirtschaftlichen Nutzung von Flächen, was zu Lebensraumzerstörung und Habitatfragmentierung führt. Die intensivere Bewirtschaftung von Gunstflächen einerseits, sowie die Flächenaufgabe unrentabler Flächen andererseits, hat schwerwiegende Folgen für Insekten, die an extensiv genutzte Kulturflächen angepasst sind, was besonders durch den abnehmenden Anteil an Spezialisten deutlich wird. Eine Region, die aufgrund des kleinräumigen Nebeneinanders von naturnahen Bereichen und anthropogen geschaffenen Kulturflächen (entlang eines großen Höhengradienten) eine wichtige Rolle für die Biodiversität besitzt, speziell als Lebensraum für Spezialisten aller Artengruppen, sind die Alpen. Auch hier stellt der landwirtschaftliche Nutzungswandel ein großes Problem dar, weshalb es einen nachhaltigen Schutz der extensiv genutzten Kulturlebensräume bedarf. Um zu klären, wie eine nachhaltige Berglandwirtschaft zukünftig erhalten bleiben kann, wurden im ersten Kapitel der Promotion die Regelungsrahmen der internationalen, europäischen, nationalen und regionalen Gesetze näher betrachtet. Es zeigt sich, dass der multifunktionale Ansatz der Alpenkonvention und des zugehörigen Protokolls „Berglandwirtschaft“ nur eine geringe normative Konkretisierung aufweisen und daher nicht im ausreichenden Maße in der Gemeinsamen Agrarpolitik der EU sowie im nationalen Recht umgesetzt werden; dadurch können diese einer negativen Entwicklung in der Berglandwirtschaft nicht ausreichend entgegenwirken. Neben diesen Rechtsgrundlagen fehlt es jedoch auch an naturwissenschaftlichen Grundlagen, um die Auswirkungen des landwirtschaftlichen Nutzungswandels auf alpine und arktische Tierarten zu beurteilen. Untersuchungen mit Charakterarten für diese Kulturräume sind somit erforderlich, wobei Tagfalter aufgrund ihrer Sensibilität gegenüber Umweltveränderungen geeignete Indikatoren sind. Deshalb wurden im zweiten Kapitel der Promotion die beiden Schwestertaxa Boloria pales und B. napaea untersucht, die für arktische und / oder alpine Grünlandflächen typisch sind. Die bisher unbekannte Phylogeographie beider Arten wurde daher mit zwei mitochondrialen und zwei Kerngenen über das gesamte europäische Verbreitungsgebiet untersucht. In diesem Zusammenhang die zwischen- und innerartlichen Auftrennungen analysiert und datiert sowie die ihnen unterliegenden Ausbreitungsmuster entschlüsselt. Um spezielle Anpassungsformen an die arktischen und alpinen Lebensräume der Arten zu entschlüsseln und die Folgen der landwirtschaftlichen Nutzungsänderung richtig einordnen zu können, wurden mehrere Populationen beider Arten freilandökologisch untersucht. Während B. pales über den gesamten alpinen Sommer schlüpfen kann und proterandrische Strukturen zeigt, ist B. napaea durch das Fehlen der Proterandie und ein verkürztes Schlupfzeitfenster eher an die kürzeren, arktischen Sommer angepasst. Obwohl beide Arten die gleichen Nektarquellen nutzen, gibt es aufgrund verschiedener Bedürfnisse Unterschiede in den Nektarpräferenzen zwischen den Geschlechtern; auch innerartliche Unterschiede im Dispersionsverhalten wurden gefunden. Populationen beider Arten können eine kurze Beweidung überleben, wobei der Zeitpunkt der Beweidung von Bedeutung ist; eine Nutzung gegen Ende der Schlupfphase hat einen größeren Einfluss auf die Population. Daneben wurde ein deutlicher Unterschied zwischen Flächen mit langfristiger und fehlender Beweidung gefunden. Neben einer geringen Populationsdichte, gibt es auf ganzjährig beweideten Flächen einen größeren Druck, den Lebensraum zu verlassen und die zurückgelegten Flugdistanzen sind hier auch deutlich größer.

Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural practices have been shaping the face of the earth, and today around one third of the ice-free land mass consists of cropland and pastures. While agriculture is necessary for our survival, the intensity has caused many negative externalities, such as enormous freshwater consumption, the loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation. Some of these externalities can potentially be ameliorated by careful allocation of crops and cropping practices, while at the same time the state of these crops has to be monitored in order to assess food security. Modern day satellite-based earth observation can be an adequate tool to quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to do so, in terms of input data, reference data and classification algorithms have been constantly improving over the past 60 years, and we live now in a time where fully operational satellites produce freely available imagery with often less than monthly revisit times at high spatial resolution. At the same time, classification models have been constantly evolving from distribution based statistical algorithms, over machine learning to the now ubiquitous deep learning. In this environment, we used an explorative approach to advance the state of the art of crop classification. We conducted regional case studies, focused on the study region of the Eifelkreis Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique role in the regional agricultural system and because of their specific phenologic characteristics we focused solely on maize fields. In the first case study, we generated reference data for the years 2009 and 2016 in the study region by drawing polygons based on high resolution aerial imagery, and used these in conjunction with RapidEye imagery to produce high resolution maize maps with a random forest classifier and a gaussian blur filter. We were able to highlight the importance of careful residual analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in spite of the severe limitations introduced by the restricted acquisition windows due to cloud coverage, high quality maps could be produced for two years, and the regional development of maize cultivation could be quantified. In the second case study, we used these spatially explicit datasets to link the expansion of biogas producing units with the extended maize cultivation in the area. In a next step, we overlayed the maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type classification in environmental protection, by producing maps of potential soil hazards, which can be used by local stakeholders to reallocate certain crop types to locations with less associated risk. In our third case study, we used Sentinel-1 data as input imagery, and official statistical records as maize reference data, and were able to produce consistent modeling input data for four consecutive years. Using these datasets, we could train and validate different models in spatially iv and temporally independent random subsets, with the goal of assessing model transferability. We were able to show that state-of-the-art deep learning models such as UNET performed significantly superior to conventional models like random forests, if the model was validated in a different year or a different regional subset. We highlighted and discussed the implications on modeling robustness, and the potential usefulness of deep learning models in building fully operational global crop classification models. We were able to conclude that the first major barrier for global classification models is the reference data. Since most research in this area is still conducted with local field surveys, and only few countries have access to official agricultural records, more global cooperation is necessary to build harmonized and regionally stratified datasets. The second major barrier is the classification algorithm. While a lot of progress has been made in this area, the current trend of many appearing new types of deep learning models shows great promise, but has not yet consolidated. There is still a lot of research necessary, to determine which models perform the best and most robust, and are at the same time transparent and usable by non-experts such that they can be applied and used effortlessly by local and global stakeholders.

The endemic argan tree (Argania spinosa) populations in southern Morocco are highly degraded due to overbrowsing, illegal firewood extraction and the expansion of intensive agriculture. Bare areas between the isolated trees increase due to limited regrowth; however, it is unknown if the trees influence the soil of the intertree areas. Hypothetically, spatial differences in soil parameters of the intertree area should result from the translocation of litter or soil particles (by runoff and erosion or wind drift) from canopy-covered areas to the intertree areas. In total, 385 soil samples were taken around the tree from the trunk along the tree drip line (within and outside the tree area) and the intertree area between two trees in four directions (upslope, downslope and in both directions parallel to the slope) up to 50 m distance from the tree. They were analysed for gravimetric soil water content, pH, electrical conductivity, percolation stability, total nitrogen content (TN), content of soil organic carbon (SOC) and C/N ratio. A total of 74 tension disc infiltrometer experiments were performed near the tree drip line, within and outside the tree area, to measure the unsaturated hydraulic conductivity. We found that the tree influence on its surrounding intertree area is limited, with, e.g., SOC and TN content decreasing significantly from tree trunk (4.4 % SOC and 0.3 % TN) to tree drip line (2.0 % SOC and 0.2 % TN). However, intertree areas near the tree drip line (1.3 % SOC and 0.2 % TN) differed significantly from intertree areas between two trees (1.0 % SOC and 0.1 % TN) yet only with a small effect. Trends for spatial patterns could be found in eastern and downslope directions due to wind drift and slope wash. Soil water content was highest in the north due to shade from the midday sun; the influence extended to the intertree areas. The unsaturated hydraulic conductivity also showed significant differences between areas within and outside the tree area near the tree drip line. This was the case on sites under different land usages (silvopastoral and agricultural), slope gradients or tree densities. Although only limited influence of the tree on its intertree area was found, the spatial pattern around the tree suggests that reforestation measures should be aimed around tree shelters in northern or eastern directions with higher soil water content or TN or SOC content to ensure seedling survival, along with measures to prevent overgrazing.

Using a dendrochronological approach, we determined the resistance, recovery and resilience of the radial stem increment towards episodes of growth decline, and the accompanying variation of 13C discrimination against atmospheric CO2 (Δ13C) in tree rings of two palaeotropical pine species. These species co-occur in the mountain ranges of south–central Vietnam (1500–1600 m a.s.l.), but differ largely in their areas of distribution (Pinus kesiya from northeast India to the Philippines; P. dalatensis only in south and central Vietnam and in some isolated populations in Laos). For P. dalatensis, a robust growth chronology covering the past 290 years could be set up for the first time in the study region. For P. kesiya, the 140-year chronology constructed was the longest that could be established to date in that region for this species. In the first 40 years of the trees’ lives, the stem diameter increment was significantly larger in P. kesiya, but levelled off and even decreased after 100 years, whereas P. dalatensis exhibited a continuous growth up to an age of almost 300 years. Tree-ring growth of P. kesiya was negatively related to temperature in the wet months and season of the current year and in October (humid transition period) of the preceding year and to precipitation in August (monsoon season), but positively to precipitation in December (dry season) of the current year. The P. dalatensis chronologies exhibited no significant correlation with temperature or precipitation. Negative correlations between BAI and Δ13C indicate a lack of growth impairment by drought in both species. Regression analyses revealed a lower resilience of P. dalatensis upon episodes of growth decline compared to P. kesiya, but, contrary to our hypothesis, mean values of the three sensitivity parameters did not differ significantly between these species. Nevertheless, the vigorous growth of P. kesiya, which does not fall behind that of P. dalatensis even at the margin of its distribution area under below-optimum edaphic conditions, is indicative of a relatively high plasticity of this species towards environmental factors compared to P. dalatensis, which, in tendency, is less resilient upon environmental stress even in the “core” region of its occurrence.

Perennial energy crops (PECs) are increasingly used as feedstock to produce energy in an environmental friendly way. Compared to traditional conversion strategies like thermal use, sophisticated technologies such as biomethanation defined different re-quirements of the feedstock. Whereas the first concept relies on dry, woody mate-rial, biomethanation requires a moist feedstock. Thus, over time, the spectrum of species used as PECs has widened. Moreover, harvest dates were adjusted to pro-vide the feedstock at suitable moisture contents. It is well known that perennial, lignocellulose- based energy crops, compared to annual, sugar- and starch- based ones, offer ecological advantages such as, inter alia, improving biodiversity in landscape, protecting soil against erosion, and protecting groundwater from nutrient inputs. However, one of the main arguments for PEC cultivation was their undemanding nature concerning external inputs. With respect to the broader spectrum of PEC spe-cies and changed harvest dates, the question arises whether the concept of PECs being low- input energy crops is still valid. This also implies the question of suitable grow-ing conditions and sustainable management. The aims of this opinion paper were to classify different PECs according to their life- form strategy, compare nutrient exports when harvested in different maturation stages, and to discuss the results in the context of sustainable PEC cultivation on marginal land. This study revealed that nutrient exports with yield biomass of PECs harvested in green state are in the same range than those of annual energy crops and therewith several times higher than those of PECs harvested in brown state or of woody short rotation coppices. Thus, PECs can-not universally be claimed as low- input energy crops. These results also imply the consequences of cultivation of PECs on marginal land. Finally, the question has to be raised whether the term PECs should prospectively be better specified in written and spoken words.