High temporal resolution records of outdoor and indoor airborne microplastics

There is increasing concern regarding airborne microplastics, but to date, studies have typically used coarse interval sampling (a day or longer) to generate deposition and concentration estimates. In this proof-of-concept study, we used a Burkard volumetric spore trap (intake 10 L min−1; recording airborne particulates onto an adhesive-coated tape moving at 2 mm hr−1) to assess whether this approach has potential to record airborne microplastics at an hourly resolution, thereby providing detailed diurnal patterns. Simultaneous sampling at outdoor and indoor locations at rural and urban sites showed clear daily and weekly patterns in microplastic concentrations which may be related to people and vehicle movement. Indoor residential concentrations of suspected microplastics were the highest (reaching hourly concentrations of 40–50 m−3), whilst rural outdoor concentrations were very low (typically 1–2 m−3 h−1). Whilst the approach shows great potential for high resolution data generation, further development is required for spectroscopic analysis and hence chemical confirmation of visual microplastic identification

Habitat heterogeneity enables spatial and temporal coexistence of native and invasive macrophytes in shallow lake landscapes

Macrophyte invasive alien species (IAS) fitness is often hypothesised to be associated with beneficial environmental conditions (environmental matching) or species-poor communities. However, positive correlations between macrophyte IAS abundance and native plant richness can also arise, due to habitat heterogeneity (defined here as variation in abiotic and native biotic conditions over space and time). We analysed survey and palaeoecological data for macrophytes in satellite lakes along the Upper Lough Erne (ULE) system (Northern Ireland, UK), covering a gradient of eutrophication and connectivity to partition how environmental conditions, macrophyte diversity and habitat heterogeneity explained the abundance of Elodea canadensis, a widely distributed non-native macrophyte in Europe. E. canadensis abundance positively correlated with macrophyte richness at both the within- and between-lake scales indicating coexistence of native and invasive species over time. E. canadensis was also more prolific in highly connected and macrophyte-rich lakes, but sparser in the more eutrophic-isolated ones. Partial boosted regression trees revealed that in eutrophic-isolated lakes, E. canadensis abundances correlated with water clarity (negatively), plant diversity (positively), and plant cover (negatively) whereas in diverse-connected lakes, beta diversity (both positively and negatively) related to most greatly E. canadensis abundance. Dense macrophyte cover and unfavourable environmental conditions thus appear to confer invasibility resistance and sufficient habitat heterogeneity to mask any single effect of native biodiversity or environmental matching in controlling E. canadensis abundance. Therefore, in shallow lake landscapes, habitat heterogeneity variously enables the coexistence of native macrophytes and E. canadensis, reducing the often-described homogenisation effects of invasive macrophytes.Output Status: Forthcoming/Available Onlin

Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, “build-it-yourself”, instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison’s cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project

Data from: Connectivity and zebra mussel invasion offer short‐term buffering of eutrophication impacts on floodplain lake landscape biodiversity

Aim: To investigate if connectivity and zebra mussel (Dreissena polymorpha) occurrence can mitigate effects of eutrophication in a lowland lake landscape. Location: Upper Lough Erne, Northern Ireland, UK. Methods: Data on environment, macrophytes and invertebrates were assembled for three basins of a large central lake and its satellite floodplain lakes via field surveys and paleolimnological analyses. Space-time interaction analyses of paleoecological data were compared pre-1950 and post-1950. Multivariate analyses examined how connectivity, environment, and zebra mussels influenced contemporary lake communities, and explain their divergence from historical communities in the past. Results: Pre-1950, we found high community variation across sites and low within-lake variation in macrophytes, but progressive eutrophication accentuated within-lake community variation after 1950. Partitioning analysis showed larger effects of connectivity than nutrient enrichment on contemporary macrophyte composition, while local effects structured invertebrate communities. Three clusters of lakes were revealed according to variation in macrophyte composition, isolation from the central lake and nutrient enrichment: Group 1– the central lake and 6 nearby lakes were meso-eutrophic (TP=66.7±47.6 μg/l; TN=0.79±0.41 mg/l) and had the highest zebra mussel abundances and organismal biodiversity; Group 2– 8 eutrophic (TP= 112±36.6 μg/l; TN=1.25±0.5 mg/l) and connected lakes; Group 3– 7 isolated and hypertrophic (TP=163.2±101.5 μg/l; TN=1.55±0.3 mg/l) lakes. Pre-1950 paleolimnological data for macrophytes and invertebrates for 5 lakes and a basin in the central lake most resembled extant lake communities of Group 1. However, paleo-records revealed that macrophytes and invertebrates subsequently converged towards those of Groups 2 and 3. Main conclusions: Our study reveals that the central “mother” lake acts as a hub for preserving biodiversity via shared hydrological connectivity with satellite lakes and high zebra mussel abundances. These may buffer the impoverishing effects of eutrophication and sustain unexpectedly high biodiversity in the short-term. Such protective buffering, however, cannot be relied upon indefinitely to conserve biodiversity

The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution

<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>:  name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to  build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong>  same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>This research was funded by Royal Botanic Gardens, Kew. Samuel Pironon and Ian Ondo are supported by the Calleva Foundation. MGU and Wiet Pot Family Foundation have funded part of the data compilation work within the framework of the Useful Plants Project. Alexander Antonelli is supported by the Swedish Research Council (2019-05191) and the Swedish Foundation for Strategic Environmental Research MISTRA (Project BioPath). Rodridgo Cámara-Leret is supported by the Swiss National Science Foundation Starting Grant (INDIGENOMICS: TMSGI3_211659)