Small scale variability in soil moisture drives infection of vulnerable juniper populations by invasive forest pathogen
Donald, Flora et al. (2020), Small scale variability in soil moisture drives infection of vulnerable juniper populations by invasive forest pathogen , Dryad, Dataset, https://doi.org/10.5061/dryad.3xsj3txc9
Hill, Preston, & Roy (2004). “
The same methods were used to map deer and sheep tracks and lie-ups across each study site and calculate distance to nearest evidence of “Grazing.activity” (m). Activity was likely underestimated across all sites, but particularly in the Cairngorms and Lake District.
The remaining covariates were obtained from existing GIS datasets.
R v. 3.4.0 (R Core Team, 2017). Layers of slope and aspect were calculated from the resampled 10 m DEM using the terrain function in the raster package (Hijmans, 2016). Altitude (m), slope (°) and aspect (°) were extracted to the XY coordinate of each sampled quadrat.
“Soil.type” was extracted to quadrat centroids from 250 m resolution datasets, obtained from a digitised version of the soil map produced by Forbes (1984), the Soilscapes dataset (Farewell et al., 2011) and the National Soil Map of Scotland (James Hutton Institute, 2011) for the Perthshire, Lake District and Cairngorms populations respectively.
National Vegetation Classification (NVC) community data, supplied by Scottish Natural Heritage (2017), was included as a covariate for the Perthshire and Cairngorms populations. The eight Perthshire communities were simplified to four broad types, amalgamated as follows: acid grasslands (U4 Festuca ovina - Agrostis capillaris - Galium saxatile; U20 Pteridium aquilinum - Galium saxatile; U24 Arrhenatherum elatius - Geranium robertianum), mires (M10 Carex dioica – Pinguicula vulgaris; M23 Juncus effusus/acutiflorus – Galium palustre) and mosaic communities suggesting transition from drier to wetter soil (U5 Nardus stricta – Galium saxatile; M15 Trichophorum germanicum - Erica tetralix wet heath) (Rodwell, 1991).
Topographic wetness index (TWI) is calculated as ln(a/tanb) where a is the specific catchment area and b is the local slope (Raduła, Szymura and Szymura, 2018). There are three stages to TWI calculation:
- Preparation of the DEM and slope (and river) rasters
- Creation of a flow accumulation raster from the DEM using a flow routing algorithm
- Creation of TWI raster using the flow accumulation and slope rasters
As there are so many pre-processing and flow routing algorithm options, a validation step was added to the workflow in order to choose the best TWI layer developed for each population on the strength of the Pearson correlation between the TWI value and mean soil moisture (% VWC) obtained for each sampled quadrat.
The water catchment for each study population was downloaded from the National River Flow Archive (https://nrfa.ceh.ac.uk/, accessed March 2018) and imported to SAGA GIS v. 2.3.2 (Conrad et al., 2015). TWI calculation using the 10 m DEM performed well in the Perthshire and Lake District populations but poorly in the Cairngorms population where 25 m resolution yielded a stronger correlation. Each DEM was clipped to extent of the catchment and prepared using the “fill sinks XXL Wang/Liu” algorithm to prevent the premature termination of drainage that can occur where local elevation minima have no lower neighbours as an artefact of DEM generation and do not relate to terrain features (Reuter et al., 2009). The algorithm progressively increases the elevation values of these “sinks” until the lowest elevation value is found from which the water can “spill” out into the rest of the cell (Wang and Liu, 2006). As the DEM used for the Cairngorms population was of a coarser resolution, explicitly incorporating the river network using the “burn stream network to DEM” tool improved the calculation. This tool lowers the value of each DEM cell that interfaces with a watercourse pixel by the minimum elevation in a neighbouring cell minus one (Conrad et al., 2015). The flow routing algorithm that performed best for the Cairngorms and Perthshire populations was Mass Flux, which divides each pixel into quarters and routes flow at that scale so flow can be routed in different directions where this is ambiguous (Gruber and Peckham, 2009). The recursive implementation of the deterministic eight algorithm, which apportions flow from each grid cell to a single adjacent cell through the steepest upslope gradient (and uses arbitrary assignment where flow direction is unclear), led to the strongest correlation and, therefore, best performing TWI layer for the Lake District population (O’Callaghan and Mark, 1984).
Bivand, R. and Rundel, C., 2017. rgeos: Interface to Geometry Engine - Open Source (GEOS). R package https://cran.r-project.org/web/packages/rgeos/rgeos.pdf
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., Böhner, J., 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
[dataset] Farewell, T.S., Truckell, I.G., Keay, C.A., Hallett, S., 2011. Use and applications of the Soilscapes datasets. http://www.landis.org.uk/downloads/downloads/Soilscapes_Brochure.pdf
[dataset] Forbes, A.R.D., 1984. Glen Artney Juniper Wood: An ecological study. Honours thesis (unpubl.). Stirling University, Stirling.
Gruber, S., Peckham, S., 2009. Chapter 7 Land-Surface Parameters and Objects in Hydrology, in: Hengl, T., Reuter, H.I. (Eds.), Geomorphometry: Concepts, Software, Applications. pp. 171–194. https://doi.org/10.1016/S0166-2481(08)00007-X
Henricot, B., Pérez-Sierra, A., Armstrong, A.C., Sharp, P.M., Green, S., 2017. Morphological and genetic analyses of the invasive forest pathogen Phytophthora austrocedri reveal that two clonal lineages colonized Britain and Argentina from a common ancestral population. Phytopathology 107, 1532–1540.
Hill, M.O., Preston, C.D., Roy, D.B., 2004. PLANTATT Attributes of British and Irish plants: status, size, life history, geography and habitats. NERC Centre for Ecology and Hydrology, Cambridge.
[dataset] James Hutton Institute, 2011. 1:250,000 Soil map (National soil map of Scotland). https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download#soilmapdata
[dataset] Moore, R.V., Morris, D.G., Flavin, R.W., 2000. CEH digital river network of Great Britain (1:50,000). https://catalogue.ceh.ac.uk/documents/7d5e42b6-7729-46c8-99e9-f9e4efddde1d
Mulholland, V., Schlenzig, A., Macaskill, G.A., Green, S., 2013. Development of a quantitative real-time PCR assay for the detection of Phytophthora austrocedrae, an emerging pathogen in Britain. For. Pathol. 43, 513–517. https://doi.org/10.1111/efp.12058
O’Callaghan, J.F., Mark, D.M., 1984. The extraction of drainage networks from digital elevation data. Comput. Vision, Graph. Image Process. 28, 323–344.
R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. URL https://www.R-project.org/.
Raduła, M.W., Szymura, T.H., Szymura, M., 2018. Topographic wetness index explains soil moisture better than bioindication with Ellenberg’s indicator values. Ecol. Indic. 85, 172–179. https://doi.org/10.1016/j.ecolind.2017.10.011
Reuter, H.I., Hengl, T., Gessler, P., Soille, P., 2009. Chapter 4 Preparation of DEMs for Geomorphometric Analysis, in: Hengl, T., Reuter, H.I. (Eds.), Geomorphometry: Concepts, Software, Applications. Elsevier B.V., pp. 87–120. https://doi.org/10.1016/S0166-2481(08)00004-4
Rodwell, J.S., 1991. British Plant Communities Volumes 1:5. Cambridge University Press, Cambridge.
[dataset] Scottish Natural Heritage, 2017. National Vegetation Classification. https://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=NVC
Wang, L., Liu, H., 2006. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int. J. Geogr. Inf. Sci. 20, 193–213.
Scottish Forestry Trust
Scottish Natural Heritage
Royal Botanic Garden Edinburgh
Scottish Forestry Trust
Royal Botanic Garden Edinburgh