DOI: 10.5061/dryad.bcc2fqzn8
title: Landscape-scale responses of freshwater biodiversity to connectivity and stressors

description: Aim There is compelling evidence that drivers and patterns of biodiversity
 and ecosystem functioning vary across multiple spatial scales, from global
 to regional, landscape and patch. However, macroecological processes
 impacting freshwater biodiversity are poorly understood compared to marine
 and terrestrial ecosystems. Despite step changes in data availability, we
 have a fragmented view beyond the local-scale of how hydrological and
 landscape connectivity interact with ecosystem stressors to shape
 freshwater biodiversity and functioning. While macroecological patterns
 can vary substantially among taxonomic groups, previous studies have
 focussed on individual habitat types, sites, or taxonomic groups within
 landscapes, hindering direct comparisons. We present a cross-landscape,
 multi-species analysis of the interactive effects of landscape and
 hydrological connectivity and stressors on standing freshwater quality and
 the diversity of several major freshwater taxonomic groups. Location Great
 Britain (United Kingdom). Time period 2000-2016 Major taxa studied
 Phytoplankton chlorophyll-a, macrophytes, molluscs, Coleoptera, Odonata,
 fish and birds. Methods Using random forests and generalised additive
 modelling, we quantified the interactive effects of landscape and
 hydrological connectivity and stressors on water quality (phytoplankton
 chlorophyll-a) and the diversity of selected taxa in standing freshwaters.
 Results We found evidence of connectivity changing from positive to
 negative relationships with biotic responses with increasing human-induced
 stress levels. Some species groups showed the inverse, reflecting
 complexities of modelling at large, cross-landscape scales. Almost all
 responses were affected by stress or connectivity, often interacting, and
 with non-linear relationships. Main conclusions Patterns in
 stressor-connectivity interactions differed across taxa, but were
 important in shaping 6 of 8 biotic responses. This emphasises the need for
 taxon-specific analyses to resolve freshwater ecological responses to
 stressors, connectivity and their interactions. Our results also highlight
 that connectivity effects must be integrated in landscape-scale,
 evidence-led decision-making, designed to reduce impacts of stressors on
 water quality and biodiversity.
description: Data collation and quality assurance
 Chlorophyll-<em>a</em> concentration
 Chlorophyll-<em>a</em> (μg/l) data are available for
 518 water bodies between 2005 and 2014 across Scotland and England. These
 data were provided by the Scottish Environment Protection Agency (for
 Scotland) and the Environment Agency (for England). Data are available for
 333 lakes and 185 reservoirs. We calculated the mean
 chlorophyll-<em>a</em> concentration over the time period, for
 each location. We removed data for water bodies that did not have
 associated connectivity and stressor metrics, and we removed reservoirs,
 resulting in a dataset of chlorophyll-<em>a</em>
 concentrations for 287 water bodies.
 Macrophytes Data are available for 2607
 water bodies in Scotland, England and Wales, for the species described in
 Table 4.2 of Willby, Pitt and Phillips (2012). Data come from publicly
 available primary datasets generated from macrophyte surveys undertaken by
 or on behalf of UK environment and conservation organisations and were
 collated by the University of Stirling. Data were collected between 1832
 and 2005. We removed records with a survey date before 1970. All surveys
 from 1970 onwards were carried out using a whole-lake or transect method.
 Some water bodies had multiple surveys, and in this case we either took
 the mean value of all data points from the most recent survey year (for
 continuous variables), or we took the maximum value of all data points
 from the most recent survey year (for count variables), or we took one of
 the values (for categorical variables, which should not change over time).
 Some parts of water bodies were identified separately in the dataset and
 these data were merged using the multiple-survey criteria above. After
 removing data points that had no waterbody ID attached, or that had IDs
 that were not in the dataset of connectivity and stressor metrics, and
 after removing data points where not all variables of interest were
 available, we retained data for 1654 water bodies. For
 each waterbody, we have data for the macrophyte species richness
 (including INNS), the number of macrophyte functional groups, the Lake
 Macrophyte Nutrient Index as an Ecological Quality Ratio (i.e. relative to
 value expected in absence of eutrophication impact), the overall quality
 score based on UK WFD method, and the morpho-edaphic index
 value. Molluscs 32,594 records of
 presences of freshwater molluscs between the years 2000 and 2016 were
 obtained from the Conchological Society of Great Britain & Ireland
 non-marine molluscs dataset (Conchological Society of Great Britain
 & Ireland, 2018), available under the CC-BY 4.0
 licence. We removed records with grid references less precise
 than 1km, or with locations outwith Great Britain, or which did not match
 with a waterbody location with connectivity and stressor data, or which
 did not have macrophyte data available. We retained data for 46 mollusc
 species across 136 1km grid cells. We calculated the total number of
 species observed in each 1km grid cell between 2000 and 2016 and used this
 as our response variable. Beetles
 227,156 records of the presence of 475 species of freshwater
 beetles between the years 2000 and 2018 were obtained from the Aquatic
 Coleoptera Conservation Trust. We removed records with grid references
 less precise than 1km, or with locations outwith Great Britain, or which
 did not match with a water body location with connectivity and stressor
 data, or which did not have macrophyte data available. We retained data
 for 395 beetle species across 435 1km grid cells. We calculated the total
 number of species observed in each 1km grid cell between 2000 and 2018 and
 used this as our response variable. Odonata
 We took a 2-stage approach to modelling Odonata species richness.
 Data for 39 non-invasive Odonata species were extracted from the British
 Dragonfly Society Recording Scheme (British Dragonfly Society Recording
 Scheme, 2018), available under the CC-BY 4.0
 licence, covering the time period 2000 to 2016. Records from
 each 1km Ordnance Survey (GB) National Grid cell that were matched to a
 water body were retained, giving 2265 1km cells over England, Wales and
 Scotland. Since Odonata species are likely to be imperfectly recorded, we
 first fitted the multispecies Bayesian occupancy model described in
 Belmont (2021, Section 2.4.1) to the data to obtain predicted Odonata
 species richness, given Odonata traits data from Powney et al. (2014),
 available under the CC0
 licence. Specifically, we used flight period (i.e. number of
 months each year in which the species flies), median body size and number
 of habitat types that each species inhabits (of lowland rivers and canals;
 bogs, moorland and lowland wet heath; ponds; lakes; and woodlands) in this
 stage of the modelling. The second stage of the modelling of Odonata
 species richness used the predicted richness from stage 1 as the response,
 in contrast to the other species responses where the observed species
 richness data were used directly. Fish
 We merged three major Great Britain data sets of freshwater fish
 species records. Firstly, we obtained Biological Records Centre (BRC)
 Freshwater Fish Recording Scheme data (Freshwater Fish Recording Scheme,
 2018), which collates records of fish sightings from recreational anglers
 and the wider public, and is available under the CC-BY 4.0
 licence. At the time of data download (31st October 2018), this
 source yielded 1,602 species presence records.  Secondly, we obtained
 304,899 professional survey and volunteer records from the Database and
 Atlas of Freshwater Fish (Biological Records Centre, 2018), which was
 downloaded on 22nd May 2018 and is available under the CC-BY 4.0
 licence. Finally, we accessed Environment Agency fish
 distribution data (Environment Agency, 2018), which was downloaded on
 10<sup>th</sup> September 2018 and is available under the
 Open Government Licence v3.0, comprising 302,292 records derived from professional surveys across England and Wales.  Our fish data set included both opportunistic presence-only records, and presence-absence data from professional surveys. Species records were assigned to 1km<sup>2</sup> grid cells spanning the land surface of Great Britain, as described in the Data subsection of the main text. Altogether, the fish data included records of 66 species, subspecies and hybrids. Since taxonomic resolution varied among datasets we merged records of subspecies to species-level prior to further analysis. We also excluded hybrid records, chiefly among the Cyprinidae and Salmonidae from further analysis. These hybrids were very rarely recorded, resulting in a minimal loss of data. Birds The dataset used to calculate bird species richness in this report was the Wetland Bird Survey (WeBS), available at https://www.bto.org/our-science/projects/wetland-bird-survey/data. Organised by the British Trust for Ornithology (BTO), alongside partners RSPB and JNCC, WeBS is a national-scale citizen science monitoring scheme, where volunteers count species at wetland sites once per month throughout the year. In this work, we considered non-zero species counts from breeding season months (April-June) over the period 2006-2016 at different wetland sites throughout England, Scotland and Wales. There were 36 species considered, chosen on the basis of knowledge of their ecology and behaviour as being most likely to follow hydrological features (on the water or in the air) in moving around the landscape within the spring and summer. Data Connectivity measures We used a suite of connectivity metrics, a selection of which are available from the UK Lakes Portal (https://eip.ceh.ac.uk/apps/lakes/). Spatial data for these metrics are available from the Spatial Inventory of UK Waterbodies at https://catalogue.ceh.ac.uk/documents/b6b92ce3-dcd7-4f0b-8e43-e937ddf1d4eb. Other datasets were collated from the UK digital river network (https://catalogue.ceh.ac.uk/documents/a78c90a2-8da4-4f0a-9c6a-c1d1a4a3c2b0) and the OS MasterMap Water (https://www.ordnancesurvey.co.uk/business-government/products/mastermap-water). All variables considered are listed in Table S1. These include the lengths of rivers (split by Strahler stream order) and canals (representing connected linear waters), the areas, perimeters and counts of lakes and ponds (representing connected standing waters), and the numbers of obstacles to fish migration, in the catchment or within buffers of different distances around each waterbody. Strahler number reflects the branching complexity of each stream segment, with Strahler number 1 indicating a headwater segment, and higher numbers indicating increasing numbers of confluences with other segments upstream. (See Figure S1.) In this study, we categorised segments with Strahler number 4 or greater as “Strahler 4+”. Obstacles to fish migration were obtained from the SEPA Obstacles to Fish Passage dataset (https://marine.gov.scot/maps/1746) for Scotland and the Environment Agency River Obstacles dataset (https://www.data.gov.uk/dataset/0df09ef3-9220-438a-8112-6879b3a51ac5/river-obstacles) for England and Wales, both of which are available under the Open Government Licence. These datasets contain information on natural and man-made barriers such as weirs and waterfalls that act as at least partial barriers to fish movement. To standardise connectivity metrics across catchments and buffers, we divided these values by their respective catchment or buffer areas. We also used a set of human vector connectivity metrics, reflecting the likelihood of visits for fishing, watersports and recreation, as described in Chapman et al. (2019). Since it was not known<em> a priori</em> at which geographical scale each of these processes might affect water quality and richness across our taxonomic groups, these metrics were derived for six geographical scales: catchment (reflecting direct upstream hydrological connectivity), and buffer sizes of 100m, 500m, 1km, 1.5km and 2km around the boundary of each waterbody (reflecting landscape connectivity for sizes 500m and above, and reflecting riparian connectivity for the 100m buffer that is most influenced by land directly bordering the waterbody). These metrics were available for waterbodies across England, Scotland and Wales. The categorical variable levels are given in Table S2. Lake, catchment, and climate features To account for the effects of lake, catchment and buffer features on biodiversity and ecosystem function, we used a set of lake, climate, catchment and buffer typology variables. These were also sourced from the UK Lakes Portal the Spatial Inventory of UK Waterbodies, the UK digital river network and the OS MasterMap Water. These included waterbody area, perimeter, fetch and altitude, and catchment and buffer mean slope, mean air temperature and mean annual rainfall. We included River Basin District (RBD, available at https://environment.data.gov.uk/dataset/779ada21-d465-11e4-8a4f-f0def148f590 for England and Wales, under the Open Government Licence, and at https://spatialdata.gov.scot/geonetwork/srv/eng/catalog.search#/metadata/354cb13c-edeb-40b0-964d-f4925d3e71e9 for Scotland, under the Open Government Licence) to account for broad spatial variation across Great Britain that would be difficult to model without using several other variables. RBDs are based on hydrological connectivity of river basins, so that they provide a suitable indicator of different land use and geology than simply using longitude and latitude. For the fish model only, we included a binary variable indicating whether the lake was hydrologically connected to the sea (“accessible to fish migration from sea”), indicating the absence of downstream barriers to fish migration. We included categorical variables such as depth type and upland/lowland classification to represent known hydrological groupings of lakes that behave similarly, with distinct relationships that are often not apparent when considering the population as a whole. (For example, lakes are known to behave differently when depth exceeds a narrow threshold, due to mixing and the ability of light to reach the lakebed, while small and large lakes tend to differ in terms of dominant habitats, with open water dominating for large lakes and littoral habitats for small lakes.) Identifying types of lake environments in which responses differ may allow more targeted management of actions to protect or restore freshwater biodiversity. More details of these lake, catchment and climate feature variables are given in Table S1. Stressors We used land use metrics as proxies for the relative intensity of human-induced pressures or aggregate stressors on each focal waterbody (henceforth referred to as stressors). As primary indicators of human modifications, we obtained the percentage agricultural land use and the percentage urban land use in the catchment and buffers of each waterbody from the UKCEH Land Cover Map 2007 (https://catalogue.ceh.ac.uk/documents/e02b4228-fdcf-4ab7-8d9d-d3a16441e23d). These stressor metrics were available for the same six geographical scales as the connectivity metrics. (More details are given in Table S1.) Biodiversity and water quality data We selected aquatic macrophytes, molluscs, beetles, Odonata, wetland birds that feed or breed by freshwater habitats and freshwater fish, as representative species of different levels of mobility and of different trophic levels (Figure 1 in the main text). We used records from several Great Britain-scale freshwater monitoring schemes (see Supplementary Material Data collation and QA section), including both presence-absence and presence-only data. The nature of species’ absences in distributional data (i.e. whether or not they are formally recorded, and how they can be interpreted) are influential to ecological inference (Lobo, Jiménez-Valverde & Hortal 2010). For presence-only data sets, we adopted a standardised approach to inferring such absences; a focal species was assumed to be absent from a specific location if it was not recorded, but other species were recorded from that same location (Kéry et al., 2010; Elliott, Henrys, Tanguy, Cooper & Maberly, 2015; Huang & Frimpong 2016)<strong>. </strong>Since Odonata species are likely to be detected imperfectly (Van Strien, Termaat, Groenendijk, Mensing & Kéry, 2010; Van Strien, Van Swaay & Termaat, 2013), we use predicted Odonata species richness obtained using the method of Belmont (2021, Section 2.4.1) as the response variable in our analyses. We used phytoplankton chlorophyll-<em>a</em> as a biological indicator of water quality. For all responses except macrophytes, we used data for 2000-2016, providing us with sufficient spatial locations for analysis. We used data from 1970 onwards for macrophytes, since these are sampled infrequently due to their low mobility that means their richness changes slowly over time. We did not see evidence of substantial temporal trends in the data. Integrating spatial data scales For chlorophyll-<em>a</em> concentrations and the taxonomic richness of macrophyte, fish and bird communities, response and explanatory variables were assigned to waterbodies. Due to the nature of their recording schemes, beetle, mollusc and Odonata records were instead assigned to 1km grid cells following the Ordnance Survey (Great Britain) National Grid (‘monads’). To reconcile these differences in recording, grid cells with at least one waterbody present had their species records spatially matched to the waterbody with the largest catchment of all waterbodies in that cell. If all waterbodies in the cell were too small to have catchments generated for them (&lt;1ha), species records within that cell were matched to the largest waterbody present. (Figure S2.) References Belmont, J. (2021). <em>Bayesian hierarchical methods for species distribution modelling under imperfect detection</em> (PhD thesis). University of Glasgow. https://theses.gla.ac.uk/id/eprint/82621 Biological Records Centre (2018). <em>Database for the Atlas of Freshwater Fishes</em> [Dataset]. doi:10.15468/3wfao2 British Dragonfly Society Recording Scheme (2018). <em>Dragonfly records from the British Dragonfly Society Recording Scheme</em> [Dataset]. doi:10.15468/cuyjyi Chapman, D. S., Gunn, I. D. M., Pringle, H. E. K., Siriwardena, G. M., Taylor, P., Thackeray, S. J., … Carvalho, L. (2020). Invasion of freshwater ecosystems is promoted by network connectivity to hotspots of human activity. <em>Global Ecology and Biogeography, 29</em>(4), 645-655. doi:10.1111/geb.13051 Conchological Society of Great Britain & Ireland (2018). <em>Conchological Society of Great Britain & Ireland: non-marine mollusc records</em> [Dataset]. doi:10.15468/6dexp9 Elliott, J. A., Henrys, P., Tanguy, M., Cooper, J., & Maberly, S. C. (2015). Predicting the habitat expansion of the invasive roach Rutilus rutilus (Actinopterygii, Cyprinidae), in Great Britain. <em>Hydrobiologia, 751</em>(1), 127-134. doi:10.1007/s10750-015-2181-9 Environment Agency (2018). <em>Freshwater Fish Counts for all Species, all Areas and all Years</em> [Dataset]. Available from: https://data.gov.uk/dataset/f49b8e4b-8673-498e-bead-98e6847831c6/freshwater-fish-counts-for-all-species-all-areas-and-all-years. Freshwater Fish Recording Scheme (2018). <em>Freshwater fish records via iRecord</em> [Dataset]. doi:10.15468/eeafla Huang, J., & Frimpong, E. A. (2016). Limited transferability of stream‐fish distribution models among river catchments: Reasons and implications. <em>Freshwater Biology, 61</em>(5), 729-744. doi:10.1111/fwb.12743 Kéry, M., Royle, J. A., Schmid, H., Schaub, M., Volet, B., Häfliger, G., & Zbinden, N. (2010). Site-occupancy distribution modeling to correct population-trend estimates derived from opportunistic observations. <em>Conservation Biology, 24</em>(5), 1388-1397. doi:10.1111/j.1523-1739.2010.01479.x Lobo, J. M., Jiménez-Valverde, A., & Hortal, J. (2010). The uncertain nature of absences and their importance in species distribution modelling. <em>Ecography, 33</em>(1), 103-114. doi:10.1111/j.1600-0587.2009.06039.x Powney, G., Brooks, S., Barwell, L., Bowles, P., Fitt, R., Pavitt, A., … Isaac, N. (2014). Morphological and geographical traits of the British Odonata. <em>Biodiversity Data Journal, 2</em>, e1041. doi:10.3897/BDJ.2.e1041 Van Strien, A. J., Termaat, T., Groenendijk, D., Mensing, V., & Kéry, M. (2010). Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists. <em>Basic and Applied Ecology, 11</em>(6), 495-503. doi:10.1016/j.baae.2010.05.003 Van Strien, A. J., Van Swaay, C. A. M., & Termaat, T. (2013). Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. <em>Journal of Applied Ecology, 50</em>(6), 1450-1458. doi:10.1111/1365-2664.12158 Willby, N., Pitt, J.-A., & Phillips, G. (2012). <em>The ecological classification of UK lakes using aquatic macrophytes</em>. Evidence SC010080/R2. Bristol: Environment Agency. https://assets.publishing.service.gov.uk/media/5a7c58d2e5274a1b00423299/LIT_7377_45a305.pdf
description: # Landscape-scale responses of freshwater biodiversity to connectivity and
 stressors
 [https://doi.org/10.5061/dryad.bcc2fqzn8](https://doi.org/10.5061/dryad.bcc2fqzn8) This repository contains data and code related to the paper “Landscape-scale responses of freshwater biodiversity to connectivity and stressors” submitted to Global Ecology and Biogeography. ## Description of the data and file structure The folder contains the following files: * **beetles_1.Rmd** (for beetles analysis) * **molluscs_1.Rmd** (for molluscs analysis) * **odonata_1.Rmd** (for odonata analysis) * **macrophytes_1.Rmd** (for macrophytes analysis) * **chl_1.Rmd** (for chlorophyll analysis) * **fish_1.Rmd** (for fish analysis) * **birds_1.Rmd** (for birds analysis) and similarly named .html files containing output of these R markdown scripts. The following files create additional output (specifically, the figures within the associated paper's main text and Supporting Information): * **gam_plots_all_responses_revision1.Rmd** (creates the files **plots_all_revision1_persp_part1.pdf** and **plots_all_revision1_persp_part2.pdf**) * **boxplots_climate_by_RBD_create.Rmd** (creates the file **boxplot_climate_by_RBD.pdf**) * **locations_plots.Rmd** (creates the file **locations_plots_gg.pdf**) PNG versions of these figures were created from the PDFs using IrfanView 64 bit version 4.62. The following files are required to run some analyses: * **helper_functions.R** (containing R functions that are used across scripts, to aid with analysis and presentation of results) * **varSum.q** (obtained from [https://www.stat.berkeley.edu/~paciorek/varSum.q](https://www.stat.berkeley.edu/~paciorek/varSum.q), containing code to allow a heteroscedastic residual variance structure in a generalised additive mixed model (GAMM): see [https://www.stat.berkeley.edu/~paciorek/computingTips/User_defined_variance_funct.html](https://www.stat.berkeley.edu/~paciorek/computingTips/User_defined_variance_funct.html) for details, also reproduced in the file **varSum_NOTES.txt**) In the "data" folder, the associated datasets are included. The "figures" folder contains relevant figures created by these scripts (as mentioned above). ### Datasets This section contains descriptions of the datasets, organised first by subfolder (i.e. by response) and then by .RData object within each subfolder. #### Beetles ##### beetles\_data\_final.RData The dataset `df.beetles.rf1` contains 435 rows and 154 columns, as follows: * `nSpecies`: Response variable, number of species of beetles. * `lake_data_Lake.area..ha.`: Lake area (ha). * `lake_data_Lake.altitude..m.`: Lake altitude (m). * `lake_data_Mean.Depth..m.`: Lake mean depth (m). * `lake_data_Volume..m3.`: Lake volume (m³). * `lake_data_Perimeter..km.`: Lake perimeter (km). * `lake_data_Fetch..km.`: Lake fetch (km) * `lake_data_Distance.to.sea...simple..km.`:. Lake straight line distance to nearest sea. * `lake_data_UK_ALT_TYPE`: Lake altitude classification (`H`: upland, &gt;800m; `L`: lowland, &lt;200m; `M`: mid altitude, 200--800m; `U`: unclassified, no data). * `lake_data_UK_SIZE_TYPE`: Lake area classification (`L`: large, &gt;50 ha area; `S`: small, 10--50 ha area; `U`: unclassified, no data, `VS`: very small, &lt;10 ha area). * `lake_data_UK_DEPTH_TYPE`: Lake mean depth classification (`D`: deep, &gt;15m; `S`: shallow, 3--15m; `U`: unclassified, no data; `VS`: very shallow, &lt;3m). * `lake_data_UK_GEOL_TYPE_`: Lake alkalinity classification (`HA`: high alkalinity, &gt;1000μEq/l; `LA`: low alkalinity, &lt;200μEq/l; `MA`: moderate alkalinity, 200-1000μEq/l; `Marl`: catchment dominated by limestone regardless of alkalinity; `U`: unclassified, no data). * `lake_data_UK_HUMIC_TYPE`: Lake humic classification, defined using mean colour (if available), or otherwise dissolved organic carbon (DOC, if available), or otherwise peat (`C`: clear, mean colour &lt;30mgPt/l or DOC &lt;4.8mgPt/l or clear water and ≤75% peat in catchment; `H`: humic, mean colour 30--90mgPt/l or DOC 4.8--11.9mgPt/l; `PH`: poly humic, mean colour &gt;90mgPt/l or DOC &gt;11.9mgPt/l; `U`: unclassified, no data). * `lake_data_Connectivity.Type`: lake connectivity classification (`Drainage`: Has both inflowing and outflowing river(s) or stream(s); `Drainage Upstream Lake`: Has both inflowing and outflowing river(s) or stream(s), and is upstream of at least one other waterbody; `Error`: Indicator that connectivity type could not be identified for that waterbody; `Headwater`: Has outflowing river(s) or stream(s), but no inflowing rivers or streams; `Isolated`: No inflowing or outflowing rivers or streams; `Sink`: Has inflowing river(s) or stream(s), but no outflowing rivers or streams). * `lake_data_Upland...Lowland`: upland/lowland classification (`Lowland`: Lake altitude &lt;200m; `Upland`: Lake altitude ≥200m). * `lake_data_WFD_River_Basin_District`: Name of the Water Framework Directive River Basin District (RBD) in which the waterbody is situated. * `catchment_Mean.altitude..m.`: Catchment mean altitude (m). * `catchment_Mean.slope..degrees.`: Catchment mean slope (degrees). * `catchment_Lake.area..`: % of catchment covered by lakes. * `catchment_Pond.area..`: % of catchment covered by ponds. * `catchment_Rivers...length..m._per_ha`: Total river length (m) in catchment divided by catchment area (ha). * `catchment_Canals...length..m._per_ha`: Total canal length (m) in catchment divided by catchment area (ha). * `catchment_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in catchment divided by catchment area (ha). * `catchment_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in catchment divided by catchment area (ha). * `catchment_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in catchment divided by catchment area (ha). * `catchment_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in catchment divided by catchment area (ha). * `catchment_Obstacles...Count_per_ha`: Number of obstacles in catchment divided by catchment area (ha). * `catchment_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in catchment divided by catchment area (ha). * `catchment_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in catchment divided by catchment area (ha). * `catchment_Lakes...Count_per_ha`: Total number of lakes in catchment divided by catchment area (ha). * `catchment_Ponds...Count_per_ha`: Total number of ponds in catchment divided by catchment area (ha). * `catchment_LCM2007...Agricultural..`: % catchment covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `catchment_LCM2007...Urban..`: % catchment covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `catchment_Mean.Temperature..2000.2016..C.`: Catchment mean air temperature (°C) for 2000--2016. * `catchment_Mean.Annual.Rainfall..2000.2016..mm.`: Catchment mean annual precipitation (mm) for 2000--2016. * `catchment_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the catchment, as defined by Chapman et al. (2020). * `catchment_Visits...Watersports`: Scaled indicator of numbers of visits for watersports purposes in the catchment, as defined by Chapman et al. (2020). * `catchment_Visits...All`: Scaled indicator of numbers of visits for all purposes in the catchment, as defined by Chapman et al. (2020). * `riparian_100m_Mean.altitude..m.`: 100m buffer mean altitude (m). * `riparian_100m_Mean.slope..degrees.`: 100m buffer mean slope (degrees). * `riparian_100m_Lake.area..`: % of 100m buffer covered by lakes. * `riparian_100m_Pond.area..`: % of 100m buffer covered by lakes. * `riparian_100m_Rivers...length..m._per_ha`: Total river length (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Canals...length..m._per_ha`: Total canal length (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Obstacles...Count_per_ha`: Number of obstacles in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Lakes...Count_per_ha`: Total number of lakes in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_Ponds...Count_per_ha`: Total number of ponds in 100m buffer divided by 100m buffer area (ha). * `riparian_100m_LCM2007...Agricultural..`: % 100m buffer covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `riparian_100m_LCM2007...Urban..`: % 100m buffer covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `riparian_100m_Mean.Temperature..2000.2016..C.`: 100m buffer mean air temperature (°C) for 2000--2016. * `riparian_100m_Mean.Annual.Rainfall..2000.2016..mm.`: 100m buffer mean annual precipitation (mm) for 2000--2016. * `riparian_100m_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the 100m buffer, as defined by Chapman et al. (2020). * `riparian_100m_Visits...Watersports`: Scaled indicator of numbers of watersports for fishing purposes in the 100m buffer, as defined by Chapman et al. (2020). * `riparian_100m_Visits...All`: Scaled indicator of numbers of visits for all purposes in the 100m buffer, as defined by Chapman et al. (2020). * `landscape_500m_Mean.altitude..m.`: 500m buffer mean altitude (m). * `landscape_500m_Mean.slope..degrees.`: 500m buffer mean slope (degrees). * `landscape_500m_Lake.area..`: % of 500m buffer covered by lakes. * `landscape_500m_Pond.area..`: % of 500m buffer covered by lakes. * `landscape_500m_Rivers...length..m._per_ha`: Total river length (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Canals...length..m._per_ha`: Total canal length (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Obstacles...Count_per_ha`: Number of obstacles in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Lakes...Count_per_ha`: Total number of lakes in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_Ponds...Count_per_ha`: Total number of ponds in 500m buffer divided by 500m buffer area (ha). * `landscape_500m_LCM2007...Agricultural..`: % 500m buffer covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `landscape_500m_LCM2007...Urban..`: % 500m buffer covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `landscape_500m_Mean.Temperature..2000.2016..C.`: 500m buffer mean air temperature (°C) for 2000--2016. * `landscape_500m_Mean.Annual.Rainfall..2000.2016..mm.`: 500m buffer mean annual precipitation (mm) for 2000--2016. * `landscape_500m_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the 500m buffer, as defined by Chapman et al. (2020). * `landscape_500m_Visits...Watersports`: Scaled indicator of numbers of watersports for fishing purposes in the 500m buffer, as defined by Chapman et al. (2020). * `landscape_500m_Visits...All`: Scaled indicator of numbers of visits for all purposes in the 500m buffer, as defined by Chapman et al. (2020). * `landscape_1km_Mean.altitude..m.`: 1km buffer mean altitude (m). * `landscape_1km_Mean.slope..degrees.`: 1km buffer mean slope (degrees). * `landscape_1km_Lake.area..`: % of 1km buffer covered by lakes. * `landscape_1km_Pond.area..`: % of 1km buffer covered by lakes. * `landscape_1km_Rivers...length..m._per_ha`: Total river length (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Canals...length..m._per_ha`: Total canal length (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Obstacles...Count_per_ha`: Number of obstacles in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Lakes...Count_per_ha`: Total number of lakes in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_Ponds...Count_per_ha`: Total number of ponds in 1km buffer divided by 1km buffer area (ha). * `landscape_1km_LCM2007...Agricultural..`: % 1km buffer covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `landscape_1km_LCM2007...Urban..`: % 1km buffer covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `landscape_1km_Mean.Temperature..2000.2016..C.`: 1km buffer mean air temperature (°C) for 2000--2016. * `landscape_1km_Mean.Annual.Rainfall..2000.2016..mm.`: 1km buffer mean annual precipitation (mm) for 2000--2016. * `landscape_1km_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the 1km buffer, as defined by Chapman et al. (2020). * `landscape_1km_Visits...Watersports`: Scaled indicator of numbers of watersports for fishing purposes in the 1km buffer, as defined by Chapman et al. (2020). * `landscape_1km_Visits...All`: Scaled indicator of numbers of visits for all purposes in the 1km buffer, as defined by Chapman et al. (2020). * `landscape_1.5km_Mean.altitude..m.`: 1.5km buffer mean altitude (m). * `landscape_1.5km_Mean.slope..degrees.`: 1.5km buffer mean slope (degrees). * `landscape_1.5km_Lake.area..`: % of 1.5km buffer covered by lakes. * `landscape_1.5km_Pond.area..`: % of 1.5km buffer covered by lakes. * `landscape_1.5km_Rivers...length..m._per_ha`: Total river length (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Canals...length..m._per_ha`: Total canal length (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Obstacles...Count_per_ha`: Number of obstacles in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Lakes...Count_per_ha`: Total number of lakes in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_Ponds...Count_per_ha`: Total number of ponds in 1.5km buffer divided by 1.5km buffer area (ha). * `landscape_1.5km_LCM2007...Agricultural..`: % 1.5km buffer covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `landscape_1.5km_LCM2007...Urban..`: % 1.5km buffer covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `landscape_1.5km_Mean.Temperature..2000.2016..C.`: 1.5km buffer mean air temperature (°C) for 2000--2016. * `landscape_1.5km_Mean.Annual.Rainfall..2000.2016..mm.`: 1.5km buffer mean annual precipitation (mm) for 2000--2016. * `landscape_1.5km_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the 1.5km buffer, as defined by Chapman et al. (2020). * `landscape_1.5km_Visits...Watersports`: Scaled indicator of numbers of watersports for fishing purposes in the 1.5km buffer, as defined by Chapman et al. (2020). * `landscape_1.5km_Visits...All`: Scaled indicator of numbers of visits for all purposes in the 1.5km buffer, as defined by Chapman et al. (2020). * `landscape_2km_Mean.altitude..m.`: 2km buffer mean altitude (m). * `landscape_2km_Mean.slope..degrees.`: 2km buffer mean slope (degrees). * `landscape_2km_Lake.area..`: % of 2km buffer covered by lakes. * `landscape_2km_Pond.area..`: % of 2km buffer covered by lakes. * `landscape_2km_Rivers...length..m._per_ha`: Total river length (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Canals...length..m._per_ha`: Total canal length (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Obstacles...Count_per_ha`: Number of obstacles in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Lakes...Count_per_ha`: Total number of lakes in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_Ponds...Count_per_ha`: Total number of ponds in 2km buffer divided by 2km buffer area (ha). * `landscape_2km_LCM2007...Agricultural..`: % 2km buffer covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `landscape_2km_LCM2007...Urban..`: % 2km buffer covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `landscape_2km_Mean.Temperature..2000.2016..C.`: 2km buffer mean air temperature (°C) for 2000--2016. * `landscape_2km_Mean.Annual.Rainfall..2000.2016..mm.`: 2km buffer mean annual precipitation (mm) for 2000--2016. * `landscape_2km_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the 2km buffer, as defined by Chapman et al. (2020). * `landscape_2km_Visits...Watersports`: Scaled indicator of numbers of watersports for fishing purposes in the 2km buffer, as defined by Chapman et al. (2020). * `landscape_2km_Visits...All`: Scaled indicator of numbers of visits for all purposes in the 2km buffer, as defined by Chapman et al. (2020). * `S.mac`: Number of species of macrophytes present in the lake. * `NFG.mac`: Number of functional groups of macrophytes present in the lake, as described in Willby, Pitt and Phillips (2012). * `LMNIEQR.mac`: Lake Macrophyte Nutrient Index as an Ecological Quality Ratio (i.e. relative to value expected in absence of eutrophication impact). * `EQR01.mac`: Lake overall quality score based on UK WFD method. * `WBID`: [UK Lakes Portal](https://uklakes.ceh.ac.uk/) waterbody ID. * `GridRef1km`: Ordnance Survey Great Britain 1km national grid reference. ##### beetles\_final\_model.RData The object `gamm3a_beetles` contains a "gamm" object comprising the final model fitted to the beetles data. ##### rf1\_beetles\_withMacrophytes.RData The object `rf1.beetles` contains the random forests output for the beetles analysis. ##### imp1\_beetles\_withMacrophytes.RData The object `imp1.beetles` contains the calculated importances for the beetles analysis. ##### mse1\_beetles\_withMacrophytes.RData The object `mse1.beetles` contains the mean squared errors for the beetles analysis. #### Birds ##### birds\_data\_final.RData The dataset `Richness_B_final` contains 1084 rows and 149 columns, with the same set of variables as in `df.beetles.rf1` (described above) except that it does not contain `nSpecies`, `S.mac`, `NFG.mac`, `LMNIEQR.mac`, `EQR01.mac` or `GridRef1km`, and it does contain the additional variable: * `Richness`: Response variable, number of species of birds. The object `lonlat.WeBS_richness_red2` contains the following columns: * `lon`: Lake longitude. * `lat`: Lake latitude. ##### birds\_final\_model.RData The object `gam4.Richness29` contains a "gam" object comprising the final model fitted to the birds data. ##### birds\_reduced\_data\_for\_final\_model.RData The dataset `Richness_MSE_29_reduced4` contains a subset of 15 variables from the `Richness_B_final` dataset, which were used in the final model. ##### rf1\_RichnessHigh\_27thJuly2021.RData The object `rf1.Richness` contains the random forests output for the birds analysis. ##### v1\_RichnessHigh\_27thJuly2021.RData The object `v1.Richness` contains the calculated importances for the birds analysis. ##### mse1\_Richness\_8thJuly2021.RData The object `mse1.Richness` contains the mean squared errors for the birds analysis. #### Chlorophyll ##### chl\_data\_final.RData There are 9 R objects within this file: * `data_Chl_Lakes_D`: A dataset of 129 observations of 118 variables. * `data_Chl_Lakes_S`: A dataset of 158 observations of 118 variables. * `data_Chl.TP_Lakes_D`: A dataset of 129 observations of 118 variables. * `data_Chl.TP_Lakes_S`: A dataset of 158 observations of 118 variables. * `data_TP_Lakes_D`: A dataset of 129 observations of 118 variables. * `data_TP_Lakes_S`: A dataset of 158 observations of 118 variables. * `lonlat_chl_tp_2`: 449 observations of 2 variables: `lon` (longitude, degrees East) and `lat` (latitude, degrees North). * `lonlat_Lakes_D`: 129 observations of 2 variables: `lon` (longitude, degrees East) and `lat` (latitude, degrees North). * `lonlat_Lakes_S`: 158 observations of 2 variables: `lon` (longitude, degrees East) and `lat` (latitude, degrees North). Each of the first 6 files contains the same set of variables as in `df.beetles.rf1` (described above) except that they do not contain `nSpecies`, `S.mac`, `NFG.mac`, `LMNIEQR.mac`, `EQR01.mac`, `GridRef1km`, the catchment and buffer mean altitude variables, and the Strahler length variables, and they do each contain one of the following additional variables: * `Chl.a.mug.l.annual.means.based.on.seasonal.means`: Response variable, chlorophyll-*a* (μg/l), for `data_Chl_Lakes_D` and `data_Chl_Lakes_S` only. * `Chla.TP`: Chlorophyll-*a* (μg/l) to total phosphorus (μg/l) ratio, for `data_Chl.TP_Lakes_D` and `data_Chl.TP_Lakes_S` only. * `TP.mug.l.annual.means.based.on.seasonal.means`: Total phosphorus (μg/l), for `data_TP_Lakes_D` and `data_TP_Lakes_S` only. ##### chl\_final\_model.RData The object `gam1_Chl_Lakes_D` contains a "gam" object comprising the final model fitted to the chlorophyll data for deep lakes. The object `gam1_Chl_Lakes_S` contains a "gam" object comprising the final model fitted to the chlorophyll data for shallow lakes. ##### chl\_reduced\_data\_for\_final\_model.RData The dataset `data_Chl_Lakes_S_reduced` contains a subset of 5 variables from the `data_Chl_Lakes_S` dataset, which were used in the final model for chlorophyll for shallow lakes. ##### v1\_Chl.TP\_Lakes\_D\_v4.RData The object `v1_Chl.TP_Lakes_D` contains the calculated importances for the chlorophyll in deep lakes analysis. ##### v1\_Chl.TP\_Lakes\_S\_v4.RData The object `v1_Chl.TP_Lakes_S` contains the calculated importances for the chlorophyll in shallow lakes analysis. ##### mse1\_Chl.TP\_Lakes\_D\_v4.RData The object `mse1_Chl.TP_Lakes_D` contains the mean squared errors for the chlorophyll in deep lakes analysis. ##### mse1\_Chl.TP\_Lakes\_S\_v4.RData The object `mse1_Chl.TP_Lakes_S` contains the mean squared errors for the chlorophyll in shallow lakes analysis. #### Fish ##### fish\_data\_final.RData The dataset `data.community.rich` contains 1011 observations of the following 40 variables: * `lake_data_Lake.area..ha.`: Lake area (ha). * `lake_data_Lake.altitude..m.`: Lake altitude (m). * `lake_data_Mean.Depth..m.`: Lake mean depth (m). * `lake_data_Volume..m3.`: Lake volume (m³). * `lake_data_Perimeter..km.`: Lake perimeter (km). * `lake_data_Fetch..km.`: Lake fetch (km). * `lake_data_Distance.to.sea...simple..km.`: Lake straight line distance to nearest sea. * `lake_data_UK_GEOL_TYPE_`: Lake alkalinity classification (`HA`: high alkalinity, &gt;1000μEq/l; `LA`: low alkalinity, &lt;200μEq/l; `MA`: moderate alkalinity, 200-1000μEq/l; `Marl`: catchment dominated by limestone regardless of alkalinity; `U`: unclassified, no data). * `lake_data_Connectivity.Type`: lake connectivity classification (`Drainage`: Has both inflowing and outflowing river(s) or stream(s); `Drainage Upstream Lake`: Has both inflowing and outflowing river(s) or stream(s), and is upstream of at least one other waterbody; `Error`: Indicator that connectivity type could not be identified for that waterbody; `Headwater`: Has outflowing river(s) or stream(s), but no inflowing rivers or streams; `Isolated`: No inflowing or outflowing rivers or streams; `Sink`: Has inflowing river(s) or stream(s), but no outflowing rivers or streams). * `lake_data_WFD_River_Basin_District`: Name of the Water Framework Directive River Basin District (RBD) in which the waterbody is situated. * `catchment_Mean.altitude..m.`: Catchment mean altitude (m). * `catchment_Mean.slope..degrees.`: Catchment mean slope (degrees). * `catchment_Lake.area..`: % of catchment covered by lakes. * `catchment_Pond.area..`: % of catchment covered by ponds. * `catchment_Rivers...length..m._per_ha`: Total river length (m) in catchment divided by catchment area (ha). * `catchment_Canals...length..m._per_ha`: Total canal length (m) in catchment divided by catchment area (ha). * `catchment_Strahler.1...length..m._per_ha`: Total length of river segments of Strahler classification 1 (m) in catchment divided by catchment area (ha). * `catchment_Strahler.2...length..m._per_ha`: Total length of river segments of Strahler classification 2 (m) in catchment divided by catchment area (ha). * `catchment_Strahler.3...length..m._per_ha`: Total length of river segments of Strahler classification 3 (m) in catchment divided by catchment area (ha). * `catchment_Strahler.4....length..m._per_ha`: Total length of river segments of Strahler classification 4 or greater (m) in catchment divided by catchment area (ha). * `catchment_Lakes...Perimeter..m._per_ha`: Total perimeter of lakes (m) in catchment divided by catchment area (ha). * `catchment_Ponds...Perimeter..m._per_ha`: Total perimeter of ponds (m) in catchment divided by catchment area (ha). * `catchment_Lakes...Count_per_ha`: Total number of lakes in catchment divided by catchment area (ha). * `catchment_Ponds...Count_per_ha`: Total number of ponds in catchment divided by catchment area (ha). * `catchment_Visits...Fishing`: Scaled indicator of numbers of visits for fishing purposes in the catchment, as defined by Chapman et al. (2020). * `catchment_Visits...Watersports`: Scaled indicator of numbers of visits for watersports purposes in the catchment, as defined by Chapman et al. (2020). * `catchment_Visits...All`: Scaled indicator of numbers of visits for all purposes in the catchment, as defined by Chapman et al. (2020). * `catchment_Obstacles...Count_per_ha`: Number of obstacles in catchment divided by catchment area (ha). * `catchment_LCM2007...Agricultural..`: % catchment covered by agriculture, as defined in the UKCEH Land Cover Map 2007. * `catchment_LCM2007...Urban..`: % catchment covered by urban land, as defined in the UKCEH Land Cover Map 2007. * `catchment_Salmon_Accessible`: Indicator of accessibility from the sea (0: not accessible by salmon from the sea; 1: accessible by salmon from the sea). * `catchment_MEAN_min_temp_2000_2016_pt.tif`: Minimum mean monthly air temperature (2000--2016) for the catchment. * `catchment_MEAN_max_temp_2000_2016_pt.tif`: Maximum mean monthly air temperature (2000--2016) for the catchment. * `catchment_MEAN_mean_temp_2000_2016_pt.tif`: Mean mean monthly air temperature (2000--2016) for the catchment. * `catchment_MEAN_mean_temp_range_2000_2016_pt.tif`: Mean annual air temperature range (2000--2016) for the catchment (i.e. the among-year mean of the within-year range in monthly air temperatures). * `catchment_MEAN_min_monthly_rain_2000_2016_pt.tif`: Minimum monthly precipitation sum across 2000--2016. * `catchment_MEAN_max_monthly_rain_2000_2016_pt.tif`: Maximum monthly precipitation sum across 2000--2016. * `catchment_MEAN_mean_sum_annual_rain_2000_2016_pt.tif`: Mean annual precipitation sum among years. * `catchment_MEAN_mean_annual_rain_range_2000_2016_pt.tif`: Mean annual precipitation range across 2000--2016 (i.e. the among-year mean of the within-year range in monthly precipitation sums). * `richness`: Response variable, number of species of fish. ##### fish\_final\_model.RData The object `gam4.richness` contains a "gam" object comprising the final model fitted to the fish data. ##### fish\_reduced\_data\_for\_final\_model.RData The dataset `data.richness.reduced_5` contains 1011 observations of 11 variables from the `data.community.rich` dataset. ##### rf1\_richness\_170621.RData The object `rf1.richness` contains the random forests output for the fish analysis. ##### v1\_richness\_170621.RData The object `v1.richness` contains the calculated importances for the fish analysis. ##### mse1\_richness\_180621.RData The object `mse1.richness` contains the mean squared errors for the fish analysis. #### Macrophytes ##### macrophytes\_final\_data.RData The dataset `data_S_3` contains 2002 rows of data for 147 variables (the same variables as `df.beetles.rf1`, with the omission of `nSpecies`, `lake_data_Lake.area..ha.`, `lake_data_Volume..m3.`, `lake_data_Fetch..km.`, `S.mac`, `NFG.mac`, `LMNIEQR.mac`, `EQR01.mac` and `GridRef1km`, and with the addition of the following two variables: * `S`: Response variable, number of species of macrophytes present in the lake. * `MEI`: Morpho-edaphic index value. The matrix `lonlat_3` contains two columns, representing the longitude and latitude (respectively) of the locations of the data in `data_S_3`. The vector `NGR_3` contains the Ordnance Survey Great Britain 1km national grid references for each of the locations of the data in `data_S_3`. ##### macrophytes\_final\_model.RData The object `gam1c_S` contains the final "gamm" object for the macrophytes data. ##### v1\_S.RData The object `v1_S` contains the calculated importances for the macrophytes analysis. ##### mse1\_S.RData The object `mse1_S` contains the mean squared errors for the macrophytes analysis. #### Molluscs ##### molluscs\_data\_final.RData The dataset `df.molluscs.rf1` contains 136 observations of the same 154 variables as in `df.beetles.rf1`, except that the final variable has been renamed from `GridRef1km` to `GridReference1km`, and the response variable `nSpecies` is the number of species of molluscs rather than beetles. ##### molluscs\_final\_model.RData The object `gamm3` is a "gamm" object that contains the final model for the molluscs analysis. ##### rf1\_molluscs\_withMacrophytesWithINNS.RData The object `rf1.molluscs` contains the random forests output for the molluscs analysis. ##### imp1\_molluscs\_withMacrophytesWithINNS.RData The object `imp1.molluscs` contains the calculated importances for the molluscs analysis. ##### mse1\_molluscs\_withMacrophytesWithINNS.RData The object `mse1.molluscs` contains the mean squared errors for the molluscs analysis. #### Odonata ##### odonata\_data\_final.RData The dataset `data_est_6` contains 2265 rows of data for 148 variables, which are the same variables as those in `df.beetles.rf1` with the omission of `nSpecies`, `S.mac`, `NFG.mac`, `LMNIEQR.mac`, `EQR01.mac`, `WBID` and `GridReference1km`, and with the addition of the following variable: * `richness.est`: Response variable, the number of species of Odonata as estimated using the method of Belmont (2021, Section 2.4.1). The object `data_var_5` is a vector of length 2265 that contains the prediction variance from the Belmont (2021, Section 2.4.1) method. The dataset `NGR.data_est_5` is a character vector of length 2265 that contains the Ordnance Survey Great Britain 1km national grid reference. The dataset `WBID.data_est_5` is a vector of length 2265 that contains the [UK Lakes Portal](https://uklakes.ceh.ac.uk/) waterbody ID. ##### odonata\_final\_model.RData The object `gamm3` is a "gamm" that contains the final model fitted to the Odonata data. ##### rf1\_odonata.RData The object `rf1.odonata` contains the random forests output for the Odonata analysis. ##### imp1\_odonata.RData The object `imp1.odonata` contains the calculated importances for the Odonata analysis. ##### imp1\_odonata\_uncond.RData The object `imp1.odonata.uncond` contains the calculated unconditional importances for the Odonata analysis. ##### mse1\_odonata.RData The object `mse1.odonata` contains the mean squared errors for the Odonata analysis. #### Climate dataset: *data\_climate\_by\_RBD.RData* The dataset `conn_catchment` contains data for 39,228 observations of the following three variables: * `WFD_RBD`: Name of the Water Framework Directive River Basin District (RBD) in which the waterbody is situated. * `Mean.Temperature.(2000-2016,.C)`: Mean air temperature (°C) for 2000--2016. * `Mean.Annual.Rainfall.(2000-2016,.mm)`: Mean annual precipitation (mm) for 2000--2016. ## Sharing/Access information The data and code are available under a CC0 licence. Full details of data sources and processing are available in the Supporting Information of the paper, and in the Methods section of the Description of this dataset.  ## Code/Software All scripts except **gam_plots_all_responses_revision1.Rmd** were run using R version 4.4.1. The script **gam_plots_all_responses_revision1.Rmd** was run using R version 4.4.2. A recent version of R is required to make use of the .R, .Rmd, .RData and .q files.