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Keywords
- biodiversity (2)
- CIR aerial imagery (1)
- LiDAR (1)
- airborne laser scanning (ALS) (1)
- dead tree (1)
- deadwood (1)
- forest dynamics (1)
- forest ecology (1)
- forest succession (1)
- forestry (1)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (4) (remove)
Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format
.csv and .SQL.
1.
The successional dynamics of forests—from canopy openings to regeneration, maturation, and decay—influence the amount and heterogeneity of resources available for forest-dwelling organisms. Conservation has largely focused only on selected stages of forest succession (e.g., late-seral stages). However, to develop comprehensive conservation strategies and to understand the impact of forest management on biodiversity, a quantitative understanding of how different trophic groups vary over the course of succession is needed.
2.
We classified mixed mountain forests in Central Europe into nine successional stages using airborne LiDAR. We analysed α- and β-diversity of six trophic groups encompassing approximately 3,000 species from three kingdoms. We quantified the effect of successional stage on the number of species with and without controlling for species abundances and tested whether the data fit the more-individuals hypothesis or the habitat heterogeneity hypothesis. Furthermore, we analysed the similarity of assemblages along successional development.
3.
The abundance of producers, first-order consumers, and saprotrophic species showed a U-shaped response to forest succession. The number of species of producer and consumer groups generally followed this U-shaped pattern. In contrast to our expectation, the number of saprotrophic species did not change along succession. When we controlled for the effect of abundance, the number of producer and saproxylic beetle species increased linearly with forest succession, whereas the U-shaped response of the number of consumer species persisted. The analysis of assemblages indicated a large contribution of succession-mediated β-diversity to regional γ-diversity.
4.
Synthesis and applications. Depending on the species group, our data supported both the more-individuals hypothesis and the habitat heterogeneity hypothesis. Our results highlight the strong influence of forest succession on biodiversity and underline the importance of controlling for successional dynamics when assessing biodiversity change in response to external drivers such as climate change. The successional stages with highest diversity (early and late successional stages) are currently strongly underrepresented in the forests of Central Europe. We thus recommend that conservation strategies aim at a more balanced representation of all successional stages.
Increasing evidence indicates that forest disturbances are changing in response to global change, yet local variability in disturbance remains high. We quantified this considerable variability and analyzed whether recent disturbance episodes around the globe were consistently driven by climate, and if human influence modulates patterns of forest disturbance. We combined remote sensing data on recent (2001–2014) disturbances with in-depth local information for 50 protected landscapes and their surroundings across the temperate biome. Disturbance patterns are highly variable, and shaped by variation in disturbance agents and traits of prevailing tree species. However, high disturbance activity is consistently linked to warmer and drier than average conditions across the globe. Disturbances in protected areas are smaller and more complex in shape compared to their surroundings affected by human land use. This signal disappears in areas with high recent natural disturbance activity, underlining the potential of climate-mediated disturbance to transform forest landscapes.
Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management.