Using lidar to assess the development of structural diversity in forests undergoing passive rewilding in temperate Northern Europe

Forested areas are increasing across Europe, driven by both reforestation programs and farmland abandonment. While tree planting remains the standard reforestation strategy, there is increased interest in spontaneous regeneration as a cost-effective method with equal or potentially greater benefits. Furthermore, expanding areas of already established forests are left for passive rewilding to promote biodiversity conservation. Effective and objective methods are needed for monitoring and analyzing the development of forest structure under these management scenarios, with airborne laser scanning (lidar: light detection and ranging) being a promising methodology. Here, we assess the structural characteristics and development of unmanaged forests and 28- to 78-year old spontaneously regenerated forests on former agricultural land, relative to managed forests of similar age in Denmark, using 25 lidar-derived metrics in 10- and 30-m grid cells. We analyzed the lidar-derived cell values in a principal component analysis (PCA) and interpreted the axes ecologically, in conjunction with pairwise tests of median and variance of PCA-values for each forest. Spontaneously regenerated forest in general had increased structural heterogeneity compared to planted and managed forests. Furthermore, structural heterogeneity kept increasing in spontaneously regenerated forest across the maximal 78-year timespan investigated. Natural disturbances showed strong impacts on vegetation structure, leading to both structural homogeneity and heterogeneity. The results illustrate the utility of passive rewilding for generating structurally heterogeneous forested nature areas, and the utility of lidar surveys for monitoring and interpreting structural development of such forests

The regional species richness and genetic diversity of Arctic vegetation reflect both past glaciations and current climate

AIM : The Arctic has experienced marked climatic differences between glacial and interglacial periods and is now subject to a rapidly warming climate. Knowledge of the effects of historical processes on current patterns of diversity may aid predictions of the responses of vegetation to future climate change. We aim to test whether plant species and genetic diversity patterns are correlated with time since deglaciation at regional and local scales. We also investigate whether species richness is correlated with genetic diversity in vascular plants. LOCATION : Circumarctic. METHODS : We investigated species richness of the vascular plant flora of 21 floristic provinces and examined local species richness in 6215 vegetation plots distributed across the Arctic. We assessed levels of genetic diversity inferred from amplified fragment length polymorphism variation across populations of 23 common Arctic species. Correlations between diversity measures and landscape age (time since deglaciation) as well as variables characterizing current climate were analysed using spatially explicit simultaneous autoregressive models. RESULTS : lts Regional species richness of vascular plants and genetic diversity were correlated with each other, and both showed a positive relationship with landscape age. Plot species richness showed differing responses for vascular plants, bryophytes and lichens. At this finer scale, the richness of vascular plants was not significantly related to landscape age, which had a small effect size compared to the models of bryophyte and lichen richness. MAIN CONCLUSION : Our study suggests that imprints of past glaciations in Arctic vegetation diversity patterns at the regional scale are still detectable today. Since Arctic vegetation is still limited by post-glacial migration lag, it will most probably also exhibit lags in response to current and future climate change. Our results also suggest that local species richness at the plot scale is more determined by local habitat factors.Compilation of the species richness data was made possible through the TFI Networks grant to CD, “Effect Studies and Adaptation to Climate Change,” under the Norforsk initiative (2011 – 2014) which supported two CBIONET-AVA workshops held in Denmark during 2013. The genetic studies were funded by the Research Council of Norway (grant nos. 150322/720 and 170952/V40 to CB).http://http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1466-82382017-04-30hb2016Plant Production and Soil Scienc

Large herbivores in novel ecosystems - Habitat selection by red deer (<i>Cervus elaphus</i>) in a former brown-coal mining area

<div><p>After centuries of range contraction, many megafauna species are recolonizing parts of Europe. One example is the red deer (<i>Cervus elaphus</i>), which was able to expand its range and is now found in half the areas it inhabited in the beginning of the 19^th century. Herbivores are important ecosystem engineers, influencing e.g. vegetation. Knowledge on their habitat selection and their influence on ecosystems might be crucial for future landscape management, especially for hybrid and novel ecosystems emerging in post-industrial landscapes. In this study, red deer habitat selection was studied in a former brown-coal mining area in Denmark. Here, natural settings were severely changed during the mining activity and its current landscape is in large parts managed by hunters as suitable deer habitat. We assessed red deer habitat preferences through feces presence and camera traps combined with land cover data from vegetation sampling, remote sensing and official geographic data. Red deer occurrence was negatively associated with human disturbance and positively associated with forage availability, tree cover and mean terrain height. Apparently, red deer are capable of recolonizing former industrial landscapes quite well if key conditions such as forage abundance and cover are appropriate. In the absence of carnivores, human disturbance, such as a hunting regime is a main reason why deer avoid certain areas. The resulting spatial heterogeneity red deer showed in their habitat use of the study area might be a tool to preserve mosaic landscapes of forest and open habitats and thus promote biodiversity in abandoned post-industrial landscapes.</p></div

Study area, situated in the middle of Jutland (right bottom).

<p>100 circular study sites with a 10 m diameter were randomly distributed over the whole area (left), red points depict findings of pellets in a study site while in orange dots, no pellets were found. Orthophoto printed under a CC BY license, with permission from COWI A/S, Denmark, original copyright 2014 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177431#pone.0177431.s005" target="_blank">S1 Text</a>) and ESRI basemaps printed under a CC BY license, with permission from ESRI and its licensors, original copyright 2014 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177431#pone.0177431.s006" target="_blank">S2 Text</a>).</p

Distribution of sample sites with pellet counts (indicating red deer presence) and without pellet counts (indicating red deer absence) across different categories of the six selected predictor variables.

<p>The bars show the number of sites observed in each category. The white end of each bar shows the proportion of sampled sites with presence of red deer pellets while the black parts depict the proportion of sampled sites with absence of red deer presence. Above each bar the percent of sites in that interval with red deer pellets is shown.</p

Deer frequency at camera sites.

<p>Measured as the number of red deer recorded divided by the amount of days the camera recorded (no. deer/day) in sampling sites with or without pellets. A t-test showed a significant difference (p = 0.00351) between the camera recorded activity in sampling sites with no pellets and camera recorded activity in sampling sites with pellets.</p

Example of digitalization of patches of landscape types inside sampling site buffer zones of 100 m radius.

<p>A shows the orthophoto with the buffer zone (red circle) before estimation of the features, and B shows the same buffer zones afterwards (dark green = needle forest, bright green = deciduous forest, blue = lakes, brown = open land, yellow = sand). Orthophoto: 16 cm resolution, printed under a CC BY license, with permission from COWI A/S, Denmark, original copyright 2014 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177431#pone.0177431.s005" target="_blank">S1 Text</a>).</p