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B4) Genetic assessment of diversity

Genetic assessment of insect diversity and functional connectivity among forest patches across the landscape

Gernot Segelbacher
Doctoral researchers: Nathalie Winiger (since 2016) & Laura-Sophia Ruppert (since 2019)

University of Freiburg, Faculty of Environment & Natural Resources, Institute of Forest Sciences,
Chair of Wildlife Ecology and Management

Background

There is growing evidence that insects are not only declining in grasslands, but also in forest ecosystems, driven by large scale effects, such as the increasing fragmentation of suitable habitat. In Central European forests existing management practices have been focusing on retention of dead wood and habitat trees, which provide habitat for saproxylic species including several that are endangered.

However, it remains unclear if this kind of management is helping to maintain species richness and genetic connectivity across forest landscapes Within that context we here focus on estimating species richness with molecular methods (metabarcoding) and identifying connectivity levels through genomics

 

Research questions and hypotheses

Here, we investigate arthropod diversity on our research plots in correlation to habitat type, retention forestry elements (such as dead wood abundance and forest heterogeneity) and connectivity of the surrounding landscape. We test how insect diversity on local plots is (i) affected by the amount of available dead wood and habitat trees on the plot level and (ii) by the surrounding matrix.  We hypothesize that:

  • Insect diversity estimates are depending on the local habitat diversity and affected by the available amount of dead wood and habitat trees.
  • Species richness is affected by the surrounding landscape matrix and the intensity of forest management.
  • Gene flow among patches is not only a function of distance among patches but also the composition of the surrounding landscape.

 

 

Approach, methods and linkages

Two different species assemblages (samples through insect traps and leaf-litter) have been collected previously and can now be further analysed. Additionally, we will now characterize TreMs invertebrate diversity through metabarcoding.

Selected species will be full genome sequenced for low coverage. Genomic data will be correlated with habitat and landscape data. The landscape matrix characteristics (tree species composition, height, horizontal and vertical structuring) have already be obtained by aerial photographs and the amount of coarse woody debris between patches is quantified by a combination of terrestrial mapping and remote sensing.

A final landscape genomics modeling approach combining both genomic and environmental variables will allow us to identify connectivity pathways across the landscape.  B4 is directly linked with B3 on sampling and analysing arthropod communities. Additionally, B4 is linked to A1 and A2 in making use of their data, mainly plot and landscape forest characteristics and with B1, B2, B5 and B6 in exchanging and combining data of the project specific taxa.

 

Findings

During the first three years, B4 started developing protocols for estimating deadwood beetle occurrence in deadwood through eDNA sampling of wood and for sampling eDNA from tree cavity mould to identify tree microhabitat inhabiting species. Several protocols have been developed to optimise sampling for flight intersection traps as well as dead wood and leaf letter when data should be used for metabarcoding.

In combination with B3, flight interception traps have been set up to determine arthropod diversity and identify key target species for the genetic connectivity studies. Based on this, eight beetle species were chosen for RAD sequencing and are now fully analysed to estimate gene flow between populations.

We are currently investigating  ground-dwelling arthropod communities in relation to habitat quality parameters like dead wood abundance/type, vegetation, soil chemistry and structural parameters like forest heterogeneity and connectivity. For this we have collect several leaf-litter sifts along a transect of each plot and processed them as bulk samples in a metabarcoding pipeline to estimate arthropod species richness.

We also have identified additional species for full genome sequencing which will allow us to estimate genetic diversity across the landscape and measure intraspecific genetic distances in addition to calling SNPs from RADseq data.

 

 

Future projects

Next PhD project B4 (starting 1 July 2022)

Initially, B4 has been focusing on assessing insect communities (through flight intersection traps) and leaf litter invertebrate communities through metabarcoding. Diversity estimates can now be related to forest structure and amounts of dead wood. Building up on the existing knowledge of insect communities key species across the landscape have now been identified, which will be the source for the upcoming project B4. Additionally we will focus on assessing species communities of TreMs.

  • Invertebrate diversity will be characterized in TreMs through metabarcoding. This will give us an additional estimate on this microhabitat, which can be related to habitat structure especially in relation to habitat trees and retention.
  • Selected species identified from the previous assessments will be full genome sequenced for low coverage. Genomic information will allow us to identify relevant SNP´s for population genomic analyses across the landscape.
  • Landscape genomic analysis combine both genomic and environmental variables to identify connectivity pathways across the landscape. Degrees of connectivity allow us to identify the spatial scale at which dispersal is relevant and relate this to forest management practice.

B4 will provide additional species richness data for insects across plots TreMs samples. These data can then be combined with already existing datasets on flying insects and soil invertebrates. This helps disentangling the effects of local and landscape patterns on insect diversity will help to measure the effects of retention foresty on forest communities as well as identify potential drivers for the ongoing insect decline in forest ecosystems. Additionally, estimated gene flow among plots allows us to quantify functional connectivity across the landscape

 

Skills required for PhD3 applicants in B4:

In addition to the general requirements described in the job advertisement, applicants who wish to apply for B4 should bring strong bioinformatics skills, specifically in in analysing large genetic datasets. We also require laboratory experience in DNA extraction, genotyping and sequencing. Experience in handling next-generation sequencing data is required and expertise in landscape genomics modelling desirable.

 

 

ConFoBi-publications by B4

 

Knuff, Anna K.; Winiger, Nathalie; Klein, Alexandra‐Maria; Segelbacher, Gernot & Staab, Michael (2019). Optimizing sampling of flying insects using a modified window trap. Methods Ecol Evol, 10, 1820–1825. www.doi.org/10.1111/2041-210X.13258.

Storch, Ilse; Penner, Johannes; Asbeck, Thomas; Basile, Marco; Bauhus, Jürgen & Braunisch, Veronika et al. (2020). Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi-scale approach. Ecology and evolution, 10, 1489–1509. www.doi.org/10.1002/ece3.6003.