Additional file 4. Correlation factors. The text-file describes how correlation factors were deri... more Additional file 4. Correlation factors. The text-file describes how correlation factors were derived and what the name components of our internal variable names mean. The spreadsheet presents the loadings of all correlation factors, which are above |0.5| and hence indicate that the respective variable is correlated to the correlation factor.
Additional file 3. Methods – technical details. The text outlines some technical details of the m... more Additional file 3. Methods – technical details. The text outlines some technical details of the methods. These descriptions help understanding in detail what has been done, but are not necessary to understand what has been measured.
Additional file 2. Focal variables as drivers of tick abundances. An extensive list of all focal ... more Additional file 2. Focal variables as drivers of tick abundances. An extensive list of all focal variables considered in this study. They are classified according to the driver groups outline in the methods section.
Additional file 1. Meta-data on study setup. A table outlining the most important meta-data, such... more Additional file 1. Meta-data on study setup. A table outlining the most important meta-data, such as center of each studied landscape window, dates of sampling, number of forest patches and a couple of landscape metrics to characterize the respective landscapes.
Additional file 13. Overall relative importance incl. sampling method. Similar to Fig. 3, this gr... more Additional file 13. Overall relative importance incl. sampling method. Similar to Fig. 3, this graph shows the relative importance values of all significant drivers, when variables controlling for the method are included in the model. Relative importance of categories of drivers in percent, including the relative importance of metrics capturing the impact of our method. See Fig. 3.
Additional file 12. Response profiles for adult abundance. Each graph has a prediction line, conf... more Additional file 12. Response profiles for adult abundance. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. Drivers of (A) Macroclimate (B) Landscape, (C) Macrohabitat, (D) Microhabitat, (E) Method control, (F) Ontogeny. 'ns' = not significant, 'disp.' = dispersules, '…(+e)' = also including evergreen species, 'abund.' = abundance, 'temp' = temperature, CV= coefficient of variation.
Additional file 11. Response profiles for nymphal abundance. Each graph has a prediction line, co... more Additional file 11. Response profiles for nymphal abundance. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. Drivers of (A) Macroclimate (B) Landscape, (C) Macrohabitat, (D) Microhabitat, (E) Method control, (F) Ontogeny. 'ns' = not significant, 'abund.' = abundance, 'tree1/tree2' = upper/lower tree-layer, FA = correlation factor, CWM = community weighted mean.
Additional file 9. Overall response profiles and model-output (also Additional files 10, 11, 12).... more Additional file 9. Overall response profiles and model-output (also Additional files 10, 11, 12). Figure for each tick stage, outlining the response for its respective linear mixed model. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. η2 represents the relative importance of the respective driver.
Additional file 6. Outline of model-building procedure (also Additional file 7). Text outlining i... more Additional file 6. Outline of model-building procedure (also Additional file 7). Text outlining in detail the model building/variable selection procedure. The figure presents a flow chart of the different steps taken.
Details on how correlation factors were derived and what the name components of the internal vari... more Details on how correlation factors were derived and what the name components of the internal variables mean. (DOCX 15 kb)
Focal variables as potential environmental drivers of B. burgdorferi prevalence. Table S1. Meta-v... more Focal variables as potential environmental drivers of B. burgdorferi prevalence. Table S1. Meta-variables. Table S2. Variables of habitat. Table S3. Variables of landscape. Table S4. Variables of macroclimate. (XLSX 40 kb)
Response profiles of infection prevalence for each significant driver. Shown are the prediction l... more Response profiles of infection prevalence for each significant driver. Shown are the prediction line, confidence band (alpha = 0.05) and the partial residuals. The driver groups are macroclimate, landscape, macrohabitat, microhabitat and ontogeny. η2 values represent the relative contribution each variable has in explaining variation in the response. Nymphal infection prevalence. (FA) = variable is a correlation factor, 'abund.' = abundance, 'disp.' = dispersules, 'cont.' = content, CWM = community weighted mean, 'reg. Leaf-dist.' = leaf distribution regular on stem. (PDF 332 kb)
S1 Location map; S2 rarefaction curves; S3 bat and bird trait tables; S4 bat STIs; S5 bat SSIs; S... more S1 Location map; S2 rarefaction curves; S3 bat and bird trait tables; S4 bat STIs; S5 bat SSIs; S6 null models for FDiv; S7 additional sampling methods
Additional file 10. Response profiles for larval abundance. Each graph has a prediction line, con... more Additional file 10. Response profiles for larval abundance. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. Drivers of (A) Macroclimate (no significant effects) (B) Landscape, (C) Macrohabitat, (D) Microhabitat, (E) Method control, (F) Ontogeny. 'ns' = not significant, 'abund.' = abundance, 'disp.' = dispersules, 'asc./pro. hab.' = ascending or prostrating habitus, 'reg. leaf-dist.' = leaf distribution regular on stem, FA = correlation factor, CV = coefficient of variation, CWM = community weighted mean.
Global forest loss and fragmentation have strongly increased the frequency of forest patches smal... more Global forest loss and fragmentation have strongly increased the frequency of forest patches smaller than a few hectares. Little is known about the biodiversity and ecosystem service supply potential of such small woodlands in comparison to larger forests. As it is widely recognized that high biodiversity levels increase ecosystem functionality and the delivery of multiple ecosystem services, small, isolated woodlands are expected to have a lower potential for ecosystem service delivery than large forests hosting more species. We collected data on the diversity of six taxonomic groups covering invertebrates, plants and fungi, and on the supply potential of five ecosystem services and one disservice within 224 woodlands distributed across temperate Europe. We related their ability to simultaneously provide multiple ecosystem services (multiservice delivery potential) at different performance levels to biodiversity of all studied taxonomic groups (multidiversity), forest patch size an...
Humans require multiple services from ecosystems, but it is largely unknown whether trade-offs be... more Humans require multiple services from ecosystems, but it is largely unknown whether trade-offs between ecosystem functions prevent the realisation of high ecosystem multifunctionality across spatial scales. Here, we combined a comprehensive dataset (28 ecosystem functions measured on 209 forest plots) with a forest inventory dataset (105,316 plots) to extrapolate and map relationships between various ecosystem multifunctionality measures across Europe. These multifunctionality measures reflected different management objectives, related to timber production, climate regulation and biodiversity conservation/recreation. We found that trade-offs among them were rare across Europe, at both local and continental scales. This suggests a high potential for 'win-win' forest management strategies, where overall multifunctionality is maximised. However, across sites, multifunctionality was on average 45.8-49.8% below maximum levels and not necessarily highest in protected areas. Theref...
Additional file 4. Correlation factors. The text-file describes how correlation factors were deri... more Additional file 4. Correlation factors. The text-file describes how correlation factors were derived and what the name components of our internal variable names mean. The spreadsheet presents the loadings of all correlation factors, which are above |0.5| and hence indicate that the respective variable is correlated to the correlation factor.
Additional file 3. Methods – technical details. The text outlines some technical details of the m... more Additional file 3. Methods – technical details. The text outlines some technical details of the methods. These descriptions help understanding in detail what has been done, but are not necessary to understand what has been measured.
Additional file 2. Focal variables as drivers of tick abundances. An extensive list of all focal ... more Additional file 2. Focal variables as drivers of tick abundances. An extensive list of all focal variables considered in this study. They are classified according to the driver groups outline in the methods section.
Additional file 1. Meta-data on study setup. A table outlining the most important meta-data, such... more Additional file 1. Meta-data on study setup. A table outlining the most important meta-data, such as center of each studied landscape window, dates of sampling, number of forest patches and a couple of landscape metrics to characterize the respective landscapes.
Additional file 13. Overall relative importance incl. sampling method. Similar to Fig. 3, this gr... more Additional file 13. Overall relative importance incl. sampling method. Similar to Fig. 3, this graph shows the relative importance values of all significant drivers, when variables controlling for the method are included in the model. Relative importance of categories of drivers in percent, including the relative importance of metrics capturing the impact of our method. See Fig. 3.
Additional file 12. Response profiles for adult abundance. Each graph has a prediction line, conf... more Additional file 12. Response profiles for adult abundance. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. Drivers of (A) Macroclimate (B) Landscape, (C) Macrohabitat, (D) Microhabitat, (E) Method control, (F) Ontogeny. 'ns' = not significant, 'disp.' = dispersules, '…(+e)' = also including evergreen species, 'abund.' = abundance, 'temp' = temperature, CV= coefficient of variation.
Additional file 11. Response profiles for nymphal abundance. Each graph has a prediction line, co... more Additional file 11. Response profiles for nymphal abundance. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. Drivers of (A) Macroclimate (B) Landscape, (C) Macrohabitat, (D) Microhabitat, (E) Method control, (F) Ontogeny. 'ns' = not significant, 'abund.' = abundance, 'tree1/tree2' = upper/lower tree-layer, FA = correlation factor, CWM = community weighted mean.
Additional file 9. Overall response profiles and model-output (also Additional files 10, 11, 12).... more Additional file 9. Overall response profiles and model-output (also Additional files 10, 11, 12). Figure for each tick stage, outlining the response for its respective linear mixed model. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. η2 represents the relative importance of the respective driver.
Additional file 6. Outline of model-building procedure (also Additional file 7). Text outlining i... more Additional file 6. Outline of model-building procedure (also Additional file 7). Text outlining in detail the model building/variable selection procedure. The figure presents a flow chart of the different steps taken.
Details on how correlation factors were derived and what the name components of the internal vari... more Details on how correlation factors were derived and what the name components of the internal variables mean. (DOCX 15 kb)
Focal variables as potential environmental drivers of B. burgdorferi prevalence. Table S1. Meta-v... more Focal variables as potential environmental drivers of B. burgdorferi prevalence. Table S1. Meta-variables. Table S2. Variables of habitat. Table S3. Variables of landscape. Table S4. Variables of macroclimate. (XLSX 40 kb)
Response profiles of infection prevalence for each significant driver. Shown are the prediction l... more Response profiles of infection prevalence for each significant driver. Shown are the prediction line, confidence band (alpha = 0.05) and the partial residuals. The driver groups are macroclimate, landscape, macrohabitat, microhabitat and ontogeny. η2 values represent the relative contribution each variable has in explaining variation in the response. Nymphal infection prevalence. (FA) = variable is a correlation factor, 'abund.' = abundance, 'disp.' = dispersules, 'cont.' = content, CWM = community weighted mean, 'reg. Leaf-dist.' = leaf distribution regular on stem. (PDF 332 kb)
S1 Location map; S2 rarefaction curves; S3 bat and bird trait tables; S4 bat STIs; S5 bat SSIs; S... more S1 Location map; S2 rarefaction curves; S3 bat and bird trait tables; S4 bat STIs; S5 bat SSIs; S6 null models for FDiv; S7 additional sampling methods
Additional file 10. Response profiles for larval abundance. Each graph has a prediction line, con... more Additional file 10. Response profiles for larval abundance. Each graph has a prediction line, confidence band (alpha = 0.05) and shows the partial residuals. Drivers of (A) Macroclimate (no significant effects) (B) Landscape, (C) Macrohabitat, (D) Microhabitat, (E) Method control, (F) Ontogeny. 'ns' = not significant, 'abund.' = abundance, 'disp.' = dispersules, 'asc./pro. hab.' = ascending or prostrating habitus, 'reg. leaf-dist.' = leaf distribution regular on stem, FA = correlation factor, CV = coefficient of variation, CWM = community weighted mean.
Global forest loss and fragmentation have strongly increased the frequency of forest patches smal... more Global forest loss and fragmentation have strongly increased the frequency of forest patches smaller than a few hectares. Little is known about the biodiversity and ecosystem service supply potential of such small woodlands in comparison to larger forests. As it is widely recognized that high biodiversity levels increase ecosystem functionality and the delivery of multiple ecosystem services, small, isolated woodlands are expected to have a lower potential for ecosystem service delivery than large forests hosting more species. We collected data on the diversity of six taxonomic groups covering invertebrates, plants and fungi, and on the supply potential of five ecosystem services and one disservice within 224 woodlands distributed across temperate Europe. We related their ability to simultaneously provide multiple ecosystem services (multiservice delivery potential) at different performance levels to biodiversity of all studied taxonomic groups (multidiversity), forest patch size an...
Humans require multiple services from ecosystems, but it is largely unknown whether trade-offs be... more Humans require multiple services from ecosystems, but it is largely unknown whether trade-offs between ecosystem functions prevent the realisation of high ecosystem multifunctionality across spatial scales. Here, we combined a comprehensive dataset (28 ecosystem functions measured on 209 forest plots) with a forest inventory dataset (105,316 plots) to extrapolate and map relationships between various ecosystem multifunctionality measures across Europe. These multifunctionality measures reflected different management objectives, related to timber production, climate regulation and biodiversity conservation/recreation. We found that trade-offs among them were rare across Europe, at both local and continental scales. This suggests a high potential for 'win-win' forest management strategies, where overall multifunctionality is maximised. However, across sites, multifunctionality was on average 45.8-49.8% below maximum levels and not necessarily highest in protected areas. Theref...
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