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The contexts of FinBIF’s emergence and developmentBuild-up of impetus and consensusA gradually strengthening drive from different sectors of society led to the eventual realisation of FinBIF as a comprehensively integrated BDDI. The earliest push came from a working group convened by the Ministry of Education and commissioned to analyse and plan the future of natural history museums (NHMs) in Finland. It identified as a goal to establish a national central NHM, one task of which would be to “act as an information centre maintaining an Automated Data Processing (ADP) based national register of collections”26. A subsequent working group commissioned to plan the central NHM concluded that one of the most urgent of the new nation-wide functions was “to create an ADP-based central registry to serve all Finnish natural history museums”27.While the central NHM was established in 1988, the proposed inclusive, national database did not materialise, but separate actors (institutions, projects, research groups) developed a variety of information systems independently. When Finland joined GBIF in 2001, an increased enthusiasm on BD informatics emerged, but significant national funding for GBIF-related activities was not mobilised. However, the demand for national progress grew rapidly with the international development of BD informatics. A project on developing the efficiency of nature conservation made recommendations on IT development, and on collating and opening data through a national solution28. The National Red List29 included recommendations on developing databases for species observations. An action plan on developing species conservation recommended a separate project for overall development of biodiversity data management, which was already close to what FinBIF later came to be30.In 2012, the Finnish Museum of natural History ‘Luomus’ successfully lobbied for a national, cross-sectoral meeting to discuss the way forward in building a national BDDI. This kick-off meeting was convened by the Ministry of the Environment on 7 September 2012. All major organisations that use, hold and/or produce biodiversity data, or fund such activities, were represented (Table 3). Subsequently, the urgency of establishing the national BDDI was re-emphasised in Finland’s Biodiversity Strategy and Action Plan31.Table 3 The organisations represented at the meeting where the establishment of a national BDDI was agreed upon on 7 September 2012.Full size tableCross-sectoral co-creation, collaboration and fundingThe host institution of FinBIF is the Finnish Museum of Natural History’Luomus’, which has led its development and the acquisition of funding, and coordinated the national and international cooperation. However, a wide national collaborative network has been an integral part of FinBIF from the start (Fig. 2), and many organisations from all sectors of society have participated in the development of FinBIF’s services and in data mobilisation. Luomus has convened seven advisory and co-creation groups in which c. 140 specialists have shared their expertise on a voluntary basis to advise policy and service development (Table 4). The wide collaborative network and the all-inclusive business model of FinBIF has helped in attracting funding for its development from many different sources with different funding criteria, such as national and EU-level research infrastructure funding, national funds for developing governance and administration, EU-funding for nature conservation actions, funds for Nordic collaboration in e-infrastructure development, private foundation grants to research and citizen science, and collaborative projects with private companies.Table 4 The expert advisory groups convened by FinBIF for co-creation of content, services, and policies. CMS = collection management system; OMS = observations management system; API = application programming interface; GIS = geographical information system; INSPIRE = infrastructure for spatial information in Europe.Full size tableIT ArchitectureFinBIF’s architecture has four key goals:

(1)

To harvest occurrence data from Finland and surrounding areas and share them as one interoperable data mass

(2)

To provide a customisable system for recording species observations

(3)

To provide a collection management system, and digitisation technologies and workflows for Finnish natural history collections

(4)

To compile, maintain, and share master data, including a taxonomic backbone of Finnish species.

These goals are accomplished by using Service-Oriented Architecture (SOA)32 and by providing over twenty individual background services that interact with each other, mostly using RESTful HTTP APIs (for explanation of REST, see33). These include: (1) A triplestore RDF database34,35 and an API for master data (taxonomy, schema vocabularies, people, collection metadata, image metadata, data download metadata) and collection specimen data; (2) a JavaScript Object Notation (JSON36) storage database and an API of primary occurrence data; (3) a Data Warehouse database, and an extract-transform-load (ETL) process and an API built on top of an HP Vertica database; (4) an Elasticsearch search engine (see https://www.elastic.co/what-is/elasticsearch) and an API for performance-critical access of primary occurrence data and taxonomy data; (5) multimedia services (images, audio storage and conversions); and (6) map services.FinBIF is provided as a service to fulfil the needs of the Finnish biodiversity data community and to share Finnish data to all interested parties. The architecture and complete software of FinBIF cannot be easily adopted by other countries or institutions. However, FinBIF maintains several software libraries that are specifically designed to be reusable by anyone; such as the specimen label generator library, and the powerful form generation tools of the Notebook OMS. Data and services can be used by third party applications via FinBIF’s public API (https://api.laji.fi).Occurrence data architectureFinBIF maintains systems for both primary and secondary occurrence data storage (Fig. 4). A primary data storage is a database or other type of storage where the data are maintained and used for the original purposes and use cases. A secondary data storage holds a copy of the primary data for distribution, analysis, and other derived purposes.Fig. 4Services, processes and data flows of FinBIF (a). The relations of services and processes to data life cycle phases as depicted in Fig. 1. Dashed lines denote planned services (b). Occurrence data flow from FinBIF’s and external actors’ primary sources through FinBIF’s data warehouse to FinBIF’s portal. CMS = Collection Management System, OMS = Observations Management System, DW = data warehouse, ETL = Extract-Transform-Load, API = Application Programming Interface, GIS = Geographical Information System, IAS = Invasive Alien Species, AI = Artificial Intelligence.Full size imageFinBIF has two IT systems for storing and maintaining primary occurrence data: the Kotka CMS for specimen data and the Notebook OMS for observational data (see below for descriptions). FinBIF also co-operates with iNaturalist and maintains iNaturalist Finland.FinBIF’s Data Warehouse harvests occurrence data from primary data sources and stores them as secondary data. The data warehouse transforms primary occurrence data into a single interoperable data mass through various ETL processes. Currently, live updates are received from 34 different data sources using eight different ETL processes. Many more datasets have been loaded using a one-time process. For primary data sources that are not databases or IT systems (for example Excel, MapInfo files), a tool is provided for harmonizing the data into FinBIF format and uploading them to the data warehouse as secondary data. The primary data remain under the ownership and management of the source, where all changes and updates are made. However, FinBIF maintains an annotation system, which stores added information about the occurrence entries (as primary data), for example identifications and quality markings by taxon experts. These annotations are stamped on top of the original data in the data warehouse to provide an enriched version of the data.Each dataset must have metadata that describe, e.g., the name, type and owner of the dataset. In the Data Warehouse, primary occurrence data, annotations, dataset metadata, the taxonomic backbone, and information on locations and people are linked to provide an enriched query service. FinBIF is a visible platform to make data FAIR16, thus increasing the prestige of the shared dataset, but the data sharer also gains through receiving quality feedback about the occurrences from the many taxon experts and volunteers that annotate the data in FinBIF. Based on the annotations, the data can be improved in the primary data source. To make data more interoperable, FinBIF encourages primary data source owners to harmonise their data with FinBIF-maintained master data, by using the same taxonomy and schema vocabularies.The Data Warehouse has two sides: public and private. The public side contains open-access data, some of which have been coarsened because of the sensitivity of the species in question or because of a research embargo, as specified in FinBIF’s data policy (https://laji.fi/en/about/2982; https://laji.fi/en/about/875). In addition, individual observers may hide, e.g., the exact location of their occurrence or their name. The restricted-access side of the Data Warehouse contains uncoarsened data. Some data sources provide a limited version to the public side and a full version to the restricted side. Government officials can access the restricted-use, uncoarsened data via a separate authorities’ portal. Researchers and other users can do so by issuing a data request (see below). Maintaining two versions of the data has required developing unique designs. They do not reveal information about sensitive species, which might endanger their preservation, but still allow joining annotations made on the restricted side so these can be shown also on the public side. Occurrences that need to be coarsened are detached from their original concept and uploaded with a random delay to the public side to make it difficult to discover the exact location from accompanying occurrences. Apart from this delay, the private and public sides are automatically fully synchronised.API and GIS services have been built on top of the Data Warehouse to allow open-access use of the data, both for the public and the restricted side. Most of the occurrence data from Finland go to GBIF via FinBIF. This data transfer is still being developed, and all datasets are not yet automatically copied to GBIF.Master data management architectureA triplestore is used in FinBIF for all small datasets, including taxon data. More specifically, the data are stored according to the RDF35 specification. An RDF Schema defines the allowed properties for each class. FinBIF’s triplestore34 implementation is an Oracle relational database with two tables (resource and statement), which provides the ability to do Structured Query Language (SQL) queries and updates. Doing small, atomic updates is easy, as only a small subset of the triplets can be updated instead of the entire data entity. Maintaining a complete record of history comes without much effort, as it can be done on an individual triplet level.As an example, the FinBIF taxon data model – including adjacent classes such as publication, person, image, and threat assessments – consists of 260 properties. If the data model were stored in a normalized relational database, there would be an estimated 56 tables, which could be difficult to maintain. Thus, in FinBIF, non-relational database solutions are preferred.IdentifiersFinBIF uses a persistent HTTP-URI identifier for all types of real-life and digital objects (specimens, occurrences, taxa, metadata, persons, organisations, information systems, etc.), as recommended by the World Wide Web Consortium37. The identifier takes the user to an ID redirect service, which redirects the user to a page that shows information about the object in human-readable format. For example, specimen identifiers redirect to information about the specimen and taxon identifiers to a page describing the taxon.The redirect service can also provide machine-readable data about the object, if the user (client software) requests that using Accept headers. Supported formats vary based on data types, e.g., for specimens, the system can offer data in RDF + XML38 and JSON-LD (see https://json-ld.org/) formats using CETAF compliant vocabulary (CETAF Specimen Preview Profile CSPP39). This is also compatible with MIDS (Minimum Information about a Digital Specimen40).If partner organisations do not provide HTTP-URI identifiers for their occurrences, FinBIF will use the persistent internal IDs of the data source to generate globally unique URI identifiers. DOI identifiers for data downloads and dataset metadata will be created in the near future.IT solutions in key services and processesFinBIF runs numerous processes to provide a rich set of services (Fig. 4). The IT solutions applied in building key services and in enabling central processes are described below. The order of the descriptions follows the data life cycle phases identified in Fig. 1.Kotka CMSKotka is one of the two primary data management systems of FinBIF. It is designed to fit the needs of different types of collections and can be further adapted when new needs arise.Kotka differs in many ways from traditional CMS solutions. It applies simple and pragmatic approaches. This has helped it grow into a nationally used system despite limited development resources – on average less than one full-time equivalent developer. The aim is to improve collection management efficiency by providing practical tools. Kotka emphasises the quantity of digitised specimens over completeness of the data. It harmonises practices by bringing all types of collections under one system; the types currently covered include zoological, botanical, mycological and palaeontological museum collections, tissue and DNA samples, and botanic garden and microbial living collections.Kotka stores data mostly in a denormalised free text format using a triplestore and a simple hierarchical data model. This allows greater flexibility of use and faster development compared to a normalized relational database. New data fields and structures can be added easily as needs arise. Kotka does some data validation, but quality control is seen as a continuous process and is mostly done after the data have been recorded into the system. The data model is loosely based on the Access to Biological Collection Data (ABCD) standard41, but has been adapted for practical needs.Kotka is a web application. Data can be entered, edited, searched, and exported through a browser-based user interface (UI). However, most users prefer to enter new data in customizable MS-Excel templates, which support the hierarchical data model, and upload these to Kotka. Batch updates can also be done using Excel. Kotka stores all revisions of the data to avoid any data loss due to technical or human error.Kotka supports designing and printing specimen labels9, annotations by external users, and handling accessions, loan transactions, and the Nagoya protocol10.Notebook OMSNotebook is the other primary data management system of FinBIF. It is a web solution for recording opportunistic as well as sampling-event-based species observations. It is being used for systematic monitoring schemes, various citizen science projects, and platforms for species enthusiasts.Notebook’s main software component is LajiForm, which is the engine that renders a given JSON Schema into a web form. LajiForm is a separate, reusable module that is fully independent from other FinBIF systems. Notebook as a whole includes other features embedded in FinBIF, such as adding complex geographical shapes to observation documents, importing data from spreadsheets, and form templates.All Notebook forms use FinBIF’s ontological schema in the JSON36 Schema format. Rendering user-friendly web forms based on a single schema is difficult, because the web form should be asking meaningful questions, instead of just rendering the schema fields according to the form description. Questions should be presented in an interactive manner. For instance, after drawing a geographical location on a map for a potential flying squirrel nesting tree, one would ask “did you see droppings at the nest?”, and answering “yes” would update the document to include a flying squirrel taxon identification with fields “breeding” and “record basis” filled in but not rendered to the form. A simpler form engine without a user interface (UI) customisation layer would just render the fields “taxon”, “breeding” and “record basis”, and the user would have no understanding why there are so many fields to fill in and how they relate to their work or study.Some Notebook forms are complex, e.g., for experienced biology enthusiasts who need a form that is advanced, customisable, and compact. Some forms are simple, e.g., for elementary school children. To tackle this, LajiForm uses a separate schema for the UI that allows everything from simple customisation, such as defining widgets for fields, changing field order or customising field labels, to more complex customisation like transforming the schema object structure, defining conditions when certain fields are shown, or if updating a field should have an effect on other fields. All the functionality is split into a loosely coupled collection of components, which can be either used as standalone components or composed together in order to achieve more advanced customisation. The programming philosophy has drawn inspiration from functional programming, which has been helpful in writing isolated, composable functionality.LajiForm is written with the JavaScript framework ‘React’. LajiForm is built on top of react-jsonschema-form (RJSF), which is an open source JSON schema web form library founded by Mozilla (see https://react-jsonschema-form.readthedocs.io/en/latest/). RJSF handles only simple customization, but it is very flexible in design and allows building extensions with features that are more powerful. Some features and design proposals were submitted to Mozilla – FinBIF is the largest outsider code contributor to RJSF, with a dozen pull requests merged.iNaturalistFiiNaturalist (https://www.inaturalist.org/) is an international observation and citizen science application and platform. The iNaturalist Network is a collection of websites that are localized to national use in c. 10 different countries. FinBIF supports the Finnish network site iNaturalist Finland through translations, instructions, communication, moderation, and user support.Finnish iNaturalist data are automatically synchronized weekly to FinBIF’s data warehouse, where they are available for local use. iNaturalist observations are linked to the observer’s own account in the FinBIF portal, if they have linked their iNaturalist and FinBIF accounts. Both features are important in encouraging observers to use iNaturalist, and to allow it to work seamlessly with other FinBIF services.Taxon EditorFinBIF has developed its own taxon database, ‘Taxon Editor’. It allows taxon specialists to maintain their own, expert-validated view of Finnish species. The aggregation of these is used as a backbone taxonomy for all FinBIF services, and the national checklist of Finnish taxa is extracted from it (see https://laji.fi/en/theme/checklist). Each taxon is given a globally unique persistent HTTP-URI identifier, which refers to the taxon concept, not to the name. The identifier does not change if the taxon concept remains unchanged. Compatibility with checklists from other countries is sought by linking taxon concepts as Linked Data.The taxon specialists (currently c. 60) maintain the taxon data using a web application. All changes made go live every night. The nightly update interval allows the specialists a grace period to make their changes. To maintain the integrity of critical data, such as lists of protected species, limitations to what the specialists can do have been imposed. Changes to critical data are carried out by an administrator.Taxon Editor has special features for linking observations to the taxonomy. These include hidden species aggregates and tools to override how a certain name used in observations is linked to the taxonomy. Misapplied names, however, remain an unresolved problem. Most observations are still recorded using plain names, but it is possible for the observer to pick a taxon concept instead, which is the most precise way. When data are published through the FinBIF portal from other information systems, the data providers can link their observations to FinBIF’s taxon concepts by providing the concept’s identifier. The ability to use taxon concepts as a basis of observations means the concepts have to be maintained over time, a task that may become arduous in the future. For further description of the functionalities of Taxon Editor, see7.Taxon Editor is also used in Red List assessments21. The threat assessment is carried out using the criteria of the International Union for Conservation of Nature (IUCN). FinBIF offers a documentation tool and an archive for the assessment, which is based on the national checklist of Finnish taxa. Information about previous assessments is available in the tool, and the assessor can copy and confirm, e.g., area of occupancy, extent of occurrence, generation length and habitat preferences of a species from the previous assessment. The service offers the possibility to add notes to most of the fields separately and commenting on the assessments by other authorised users. In line with the IUCN instructions, the tool automatically chooses the criteria leading to the highest possible threat category of criteria filled out for the species, although the assessor confirms the final evaluation. In several fields, the tool automatically checks the validity of values entered, e.g., criteria, threat category, length of observation period, causes of threat, and current threat factors. The tool includes necessary fields for backcasting the categories of previous assessments to count the Red List Index42. There is also a possibility to do regional threat assessments. Data are stored to the triplestore34, which archives the history of all changes.Media serviceThe FinBIF media service currently supports receiving, transforming, storing and serving images and audio. For images, the original media is stored, and the service generates a smaller JPEG version to be used in the web. The service also generates different sized thumbnails. Only a handpicked set of Exchangeable image file format (Exif43) metadata is kept, so that the metadata would not leak location information about sensitive species occurrences. For audio, wav and mp3 formats are supported. The original file is processed (cleaned) to prevent any malicious content. Mp3, wav and a spectrogram are generated and stored. Support for the International Image Interoperability Framework (IIIF; https://iiif.io/about/) standard is under planning.Data upload serviceMany occurrence datasets are not yet maintained in modern IT systems or databases, which could use an API to transfer data. FinBIF is able to receive data from Excel and GIS systems as secondary data to its data warehouse. First, metadata are generated about the dataset, and selected users are given access to upload data to that dataset. Then the owner of the data must transform the data to a row/column-based table, i.e., MS-Excel or tab-separated value (TSV) file. Each row has one occurrence and must have an ID that is unique to that dataset. The dataset owner then proceeds to upload the row/column-based file using a Web UI, in which the owner maps the fields and values of their data to FinBIF schema fields and values. Updates are done by re-uploading the entire dataset or only a part of it. Deletions are handled by a specific column that annotates that occurrence as deleted.Taxonomy backendThe taxonomy backend transfers the data created in Taxon Editor to an Elasticsearch search engine on a nightly basis. FinBIF’s API is built on top of the data in Elasticsearch. GBIF’s taxonomic backbone is currently being integrated into FinBIF’s taxonomy for taxa with occurrences but no taxonomy in FinBIF. This allows FinBIF users to browse a taxonomy that is a combination of the FinBIF and GBIF taxonomies. The taxonomy backend could harvest descriptions and images from other sources, but currently these functionalities are disabled because of data quality problems.Collection metadata backendCollection and dataset metadata are maintained primarily using the Kotka CMS, but metadata are also harvested from partner organisations’ metadatabases. The metadata of a collection contain the taxonomic, geographic and temporal coverage of the dataset, as well as information about its quality using a three-level grading: (1) Professional; (2) Expert hobbyist / expert curated; (3) Citizen science / mostly non-curated. Occurrence data can be filtered in the FinBIF data warehouse based on these levels. Each occurrence also has its occurrence-specific quality grading, which is different from the dataset grading. The collection metadata define, e.g., the people who handle data requests, which are done in FinBIF’s restricted data request service.Taxonid.orgFinland and Sweden are piloting, with a subset of taxonomic groups, to connect national checklists using Linked Open Data standards44 and agreed vocabularies. By using HTTP-URI as globally unique, persistent identifiers for taxon concepts45, the service provides both human-readable (Hypertext Markup Language, HTML) and machine-readable (Extensible Markup Language, XML) responses for client requests via a central checklist (http://taxonid.org/). Future steps include linking the national lists with the global reference checklist developed by Catalogue of Life (https://www.catalogueoflife.org/). Currently the service includes only taxonomic information, but the eventual goal is to share information also on genetics, images, and traits, as well as on conservation status and observations, in a standardised way. The work was part of the DeepDive project which was funded by the Nordic e-Infrastructure Collaboration (https://neic.no/deepdive/). The vision is to establish a regional infrastructure network consisting of Nordic and Baltic data centres and information systems, and to provide seamlessly operating regional data services, tools, and virtual laboratories.Application programming interface (API)A fundamental goal of FinBIF is to provide all data in machine-readable formats, so that the data and services can be used by third party applications. This can be accomplished using FinBIF’s public API (https://api.laji.fi). It provides access to all data available in FinBIF. The FinBIF portal is built using solely the public API, which should ensure the API is robust, of high performance, reliable and easy to use.GIS servicesFinBIF aims to share occurrence data in GIS formats (WFS, WMS; see https://www.ogc.org/standards/). These services are currently under construction.Restricted data request serviceThe restricted-use data that FinBIF harbours play a crucial role in, e.g., land-use decisions and conservation. To make these data findable, and conditionally accessible, through the public portal, a Restricted-use Data Request Service (RDRS) has been employed. It allows any user with a valid justification to request access to restricted-use datasets. After selecting the required compilation of data, the user submits a standardised form with the required information. The often multiple data owners receive a notification and a request to log in to the data owners’ section of the portal to scrutinise the request. The owners may discuss the request privately among themselves to facilitate decisions. In case of discrepancy by separate owners, the user who submitted the request may still download the released part of the data.Occurrence annotationFinBIF maintains an annotation system, which allows adding information to all occurrence records in the data warehouse. Any registered user can add comments and mark occurrences as needing verification. Trusted users can be given an expert annotator status, which allows them to have more effect on how the occurrences are shown. Experts can change the identification (taxon) that is displayed by default, grade occurrences based on their quality (verified, unreliable, erroneous), and override other annotations. However, annotations never change the original occurrence, which is always kept available. Data owners can be notified about annotations regarding their data, so that they can check and correct possible errors in the primary data source.R-libraryFinBIF provides an R programming language interface to the FinBIF API. The FinBIF R package makes the publicly available data in FinBIF accessible from within R. Biodiversity information is available on taxonomy and taxon occurrence. Occurrence data can be filtered by taxon, time, location and other variables. The data accessed are conveniently preformatted for subsequent analyses. Documentation and download can be found at https://luomus.github.io/finbif/.Map servicesFinBIF has built a JavaScript map service on top of a popular Leaflet library. It provides import and export in various formats, support for the Finnish national coordinate systems and a legacy coordinate grid layout, which is still widely used as a basis of monitoring schemes, though no longer officially supported by Finland’s geographical authorities. The tool provides a rich collection of national and international map layers that are useful in reporting and evaluating occurrence data. The tool also allows calculating lengths and areas as well as drawing complex geographical shapes (e.g. survey polygons and polylines with buffer areas).AI-based species identificationFinBIF cooperates with projects that utilise machine learning in species identification and data classification. They involve creating (semi-)automated identification pipelines for Finnish fungus species from images, and bat and bird species from audio recordings. FinBIF has provided images of fungi, is building a crowd-sourcing platform on which bird experts can produce training material, and aims to implement the Animal Sound Identifier software46 for building on-line identification services.User authenticationFinBIF provides an authentication and authorisation service that can be used also by third party applications and websites. Separate authentication flows exist for websites and native applications. Users can create one FinBIF account per e-mail address and associate multiple authentication methods and other user identities to their account. For example, users can login with Google or Facebook credentials and associate their iNaturalist, Finnish Wildlife Agency and Finnish Bird Ringing user identities to their FinBIF account. This enables users to see the occurrence data they have entered into various systems as their own occurrences in the FinBIF portal.Servers, service continuity, backup and disaster recoveryFinBIF uses services provided by CSC, the Finnish IT centre for science (https://www.csc.fi) and the University of Helsinki IT department. Both provide FinBIF several OpenStack (https://www.openstack.org/) based virtual servers and an OpenShift (https://www.openshift.com/) cloud platform. FinBIF is not committed to any certain Service Level Agreement (SLA), but its availability is above 95%. All primary data gathered by FinBIF are professionally backed up by the University of Helsinki. CSC IDA (https://www.fairdata.fi/en/ida/) is used to archive larger datasets, such as specimen images. The CSC digital preservation service (https://www.fairdata.fi/en/fairdata-pas/) will be used for long-term archiving.Development methodologiesAgile developmentThe FinBIF in-house ICT team uses a three-level development process. The first level provides capacity to do long-term planning. Epic level requests (entirely new parts of the infrastructure, inclusion of new data sources, new monitoring schemes) are compiled, prioritised and to some degree scheduled using a Trello board. The aim is to open this board for all stakeholders and the public, so that interested parties can track progress and focus of development. This board is maintained in biweekly sessions.The second level is used to direct current focus. A combination of Kanban (see https://www.atlassian.com/agile/kanban) and Scrum47 methodologies is used. Every two weeks the status of ongoing epics is checked, and the epics for the next two weeks are decided. Unlike in Scrum, there is no attempt to estimate or set goals for the sprints. Rather, a Kanban-like free-flowing system is used, where things take as long as they take.The third level is the implementation process for individual epics. Like in Scrum, each epic has a named product owner (PO). The PO communicates with the stakeholders and defines user stories. The development team turns the user stories into smaller tasks, which are maintained in a Pivotal tracker backlog (www.pivotaltracker.com).It is hoped that, in the future, developers from partner organisations could be attracted to participate in the development process.Data acquisition for this paperComparative data for other BDDIsThe data shown in Table 1 and supporting the drawing of Fig. 1 were acquired by searching for information on the public internet portals of the infrastructures between July 2019 and March 2020. The data were then sent to the representatives of the infrastructures for checking and possible corrections. Altogether 27 infrastructures (eight global and 19 national or institutional) were contacted, and answers were received from 23 (six global and 16 national or institutional) infrastructures. The following information was gathered.
What is:

The taxonomic coverage of your infra?

Current number of records?

Number of species you have information on?

Does your infrastructure share the following types of data and information:

Natural history collection data?

Opportunistic observation data?

Observation data collected in systematic monitoring schemes?

Verbal descriptions of species?

Standardized taxonomic core data?

DNA-barcodes (or links to BOLD database)?

Does your infrastructure provide the following kinds of user services:

Possibility to enter event-based data, meaning several species observations linked together?

A Collection Management System?

IUCN red-listing tools?

Red-list classifications or administrative statuses of species?

e-lab services? What kind of?

Is the infrastructure designed to enable:

Data generation, such as digitization of natural history specimens?

Collation / aggregation of data?

Entering observations by citizen science users?

Annotation of records/identifications?

FinBIF’s user dataTotal numbers of users and use sessions were acquired from Google Analytics (https://analytics.google.com) for the website laji.fi in its entirety. To determine which traffic belongs to which user, Google Analytics sends a unique identifier associated with each user with each hit. This is accomplished via a Client ID, a unique, randomly generated string that gets stored in the browser’s cookies, so subsequent visits to the same site can be associated with the same user. Using cookies allows identifying unique users across browsing sessions, but it cannot identify unique users across different browsers or devices. Hence, the user numbers presented here are slight overestimates of true numbers of different people using the service. The use sessions are total number of sessions within the given range of dates. A session is the period during which a user is actively engaged with the website. All usage is associated with a session.The number of registered users comes from FinBIF’s own registry. Until the end of 2019, users had to register at the service to be able to download data from laji.fi to their own device. Subsequently, the requirement has been relaxed so that registration is required only to obtain a citable persistent unique HTTP-URI identifier for a downloaded data batch, but non-citeable so-called light downloads can be done without registration. Registration is also required to be able to record data to FinBIF’s primary data management systems and to annotate records at https://laji.fi/.Numbers of data downloads and downloaded data points are logged by FinBIF itself. As of the beginning of 2020, the numbers reported in Fig. 3b include light downloads. More

‘Foraging habitat suitability’ is a reference to the favorability of a patch of land for day-to-day subsistence. Here, suitability is an index value ascribed to each potential foraging patch (grid cell) captured in a raster image, based on terrain movement costs and the proximity of each patch to water and green vegetation. We constructed our foraging habitat suitability model using satellite-derived environmental data, digital terrain information and anthropological field data on foraging range (Fig. 3). The model’s environmental foundation is based on more than two decades of continuous near bi-weekly Landsat-5 satellite observations, allowing for the systematic detection and measurement of water recurrence and vegetation condition for every 30-x-30 m image pixel. This period of observation is long enough to observe multiple fluctuations in this highly variable environment and to not be restricted to a single short-term climatic state, such as a bushfire or drought. Therefore the time frame provides a reliable observation and measurement of maximum vegetation greenness, regardless of temporary drops in NDVI. Similarly, maximum extent and occurrence of surface water is systematically measured through long-term satellite observations, avoiding measurements only of phases of drought or irregular rainfall. For this reason our model focuses on maximal values to represent the best environmental conditions that would have been available for past foraging activities since the last glacial, based on the contemporary climatic regime.Figure 3A satellite derived model of foraging habitat suitability for the Australian Western Desert. Foraging habitat suitability is highly variable within IBRA boundaries and throughout the Western Desert. Several massive areas of low-ranked foraging habitats are evident throughout the region. IBRA codes and excavated rockshelter sites (lime green- numbered) are defined in the Fig. 1 caption. Map created in ESRI ArcGIS Desktop 10.5.1 (https://desktop.arcgis.com), linear stretch (1.0%) visualization. See “Methods” section for source raster information.Full size imageThe model also uses the ALOS World 3D 30 m digital elevation data product to quantify terrain ruggedness across the study area46. Terrain ruggedness is a geomorphometric measure of land surface ruggedness, where elevation variability is used to infer ease of traversal when walking between locations in the landscape. Terrain ruggedness is suggestive of potential energy expenditure, assuming that increasingly rugged terrains necessitate higher levels of physical activity and caloric intake. Here, we integrated measures of ruggedness with environmental satellite data, providing an indication of which patches of vegetation and water are most easily accessed in regards to minimum changes in elevation.Walking time to observed surface water is the final spatial parameter incorporated with the model. It is calculated using Tobler’s47 hiking algorithm and information on daily foraging practices. Historic anthropological data indicates Western Desert foraging activities typically operated for 4–6 h each day1,48, with foragers moving up to a day from ephemeral water sources in their food quest1. In accordance with these ethnographic statements we spatially delineated land areas where regular foraging activities may have occurred by first calculating the walking time from water, then weighting all areas that were less than 8 h walk from water more heavily in the input which went into our final suitability model. Since resources are said to be permanent in uplands4,5, we assume mountainous refugia were always suitable foraging habitats, so these refuge areas have been masked and removed from consideration (see mountain ranges in Fig. 3).Appropriate elements from all of the aforementioned satellite datasets were combined to produce our foraging habitat suitability model (Fig. 3). The ~ 30 m spatial resolution of the data facilitates the construction of a spatially-explicit, geographically broad, yet fine-grained ecological model to visually observe and critically appraise foraging habitat suitability at a variety of scales, offering new perspectives on regional human behavioral ecology. The model provides a continuous ranking of the relative foraging value for each landscape patch (or 30 m grid cell in this instance). Interpretation of patch values is based on the proposition that foragers know the conditions in all parts of the landscape they visit, and they organized their daily foraging movements in accordance with the factors outlined above.Our habitat suitability model illustrates the highly varied favorability of foraging patches across the Western Desert (Fig. 3), as calculated from data on natural resource distribution, terrain attributes, and daily foraging range. The model is conceptual, based on quantitative environmental variables that have been well documented to influence desert foraging activity. In regards to the model’s robustness, the input variables are equally weighted and statistically independent (see “Methods” section). The equal weighting reflects the concepts and assumptions of earlier research, particularly of existing landscape mapping, offering a coherent and consistent modelling approach. Advanced mathematical modelling, incorporating sensitivity analysis49,50, could be used to modify the weighted contribution of each variable, and such modelling will be the subject of future papers. Until more detailed knowledge of past forager land use and contemporary resources becomes available there is little benefit in arbitrarily substituting other input values in our model.The model comprises a matrix of nearly 1.3 billion data cells, each of which has been individually analyzed and ascribed each foraging patch a value indicative of potential habitat suitability. The computational power required to statistically analyze the dataset is massive, so to simplify computing and broadly characterize intraregional variation, we scaled up using nationally defined IBRA subregions. We used IBRA boundaries to group and rank the patch values into low, moderate, and high foraging habitat suitability classes and then calculated the land area occupied by each class (Fig. 4 and Table S1). Higher-ranked localities are well positioned in relation to suitable resources and easily traversed terrains. Lower-ranked patches are considered poorly-suited habitats due to their considerable distance from water and plant resources, and they are in comparatively rugged terrains. Areas deemed to have moderate foraging suitability have mixed accessibility to resources and variable terrain ruggedness.Figure 4Percentage of land area (km2) occupied by low, moderate, and high-ranked habitat suitability patches for the eleven largest IBRA bioregions of the Western Desert (Table S1). The histogram is ordered left to right based of the percentage of high-ranked foraging habitat within each bioregion. The percentages for the entire Western Desert are presented on the far right.Full size imageThe results show that during times of maximum water abundance and vegetation greenness, 36.6% of the Western Desert has high-ranked habitat suitability (Fig. 4 and Table S1). Moderately suitable areas constitute 48.9% and low-ranked patches encompass 13.1%. Breaking these findings down further, we calculated the ranked land areas for the eleven largest IBRA subregions ( > 10,000 km2) of the Western Desert (Fig. 4 and Table S1). At a broad bioregional level, intra-upland zones (Fig. 4; CER01) and desert plains (Fig. 4; GAS02, GVD01, and NUL01) offer a greater percentage of high-ranked foraging habitats. Bioregions dominated by dunefields have considerably less high ranked land areas compared to uplands, plains, and areas of low relief, although it is important to note that there is also considerable patchiness amongst suitable foraging areas in sandridge desert regions (Fig. 3). For instance, the centrally located Gibson Desert dunefield area (Fig. 4; GID02) has very little area of high-ranked habitat (10.8%), which is far less than other sandridge desert bioregions (Fig. 4; GSD02, LSD02, GVD02, GVD03, and GVD04) where high-ranked suitability areas range between 22.3 and 39.9%. Similarly, the Gibson Desert stony desert bioregion (Fig. 4; GID01), which is dominated by lateritic surface gravels, records only 25.5% high-ranked habitat areas. Thus, at a coarse-grained scale, it seems that some central core regions of the Western Desert are more environmentally hostile and offer less high-ranked foraging opportunities compared to more peripheral bioregions. This generality does not imply such areas were unutilized by desert peoples, but rather some areas were on average volatile and had low productivity.Foraging potential is highly varied amongst bioregions and land systemsWhen viewed at a fine-grained scale, our model clearly shows that there is an uneven gradient of suitable foraging habitats across the Western Desert, and foraging suitability trends are not pervasive throughout particular bioregions or land systems (Fig. 3). Away from montane uplands, water permanence is always temporary, and land systems with low topography, such as plains, stony plains, and sandridge desert, have highly varied foraging suitability, even when characterized in the best environmental conditions.The implications of this variation are important to understanding human ecology of the ethnohistoric period and the late Holocene archaeological record of the past 2000 years, when climatic conditions and landscapes were much like the present day36,40,42. Many scholars have noted that the historic desert peoples were familiar with the distribution of regional natural resources1,5,7. It has been argued that resource knowledge was articulated with socioeconomic strategies, and that groups routinely utilized all areas of the Western Desert during times of good rainfall and resource abundance. However, our suitability model reveals that there are large, expansive areas of the desert landscape that would have presented substantial challenges for survival, even in the best environmental circumstances (Fig. 3).Our model further suggests that low-ranked locations of foraging suitability were always below average productivity and were always comparatively unsuited as foraging habitats. To carry out that measure, we needed an independent indicator of land productivity, NDVI. We used satellite observations of maximum vegetation greenness to quantify how land productivity differs amongst low, moderate, and high ranked foraging habitats (Table S2). Variation in mean (µ) NDVI for each habitat class illustrates how land productivity differs within and amongst the most prominent Western Desert bioregions (Fig. 5). Given the below average NDVI of all low-ranked desert lowlands, we hypothesize that broad clusters of extremely unsuitable localities would be unlikely to provide adequate returns (Fig. 5), even when foragers were pursuing low-variance or lower quality resources51. Based on the distribution of low-ranked patches (Fig. 3), we agree with earlier research that the entire desert region was not equally economically viable for foraging, and that substantial tracts of land were not economically attractive to resident populations4,5,14,32. We also recognise that the distribution of massive-sized sub-optimal patches may be an important factor shaping the patterns of movement through the landscape, with foragers potentially preferring movement along high suitability corridors. However, unlike earlier research, our suitability model shows that unfavorable foraging areas are not correlated with large units of biogeography alone. Our model depicts the environmental variability of the Western Desert at a much higher resolution than its predecessors, revealing several massive land tracts where unfavorable foraging conditions occur (Fig. 3). If ethnographic patterns of land use were in place, we predict that many of these large areas would have been rarely utilized or perhaps some were purposefully avoided due to known deficiencies in the resource energy base12. This proposition is readily testable because it predicts that archaeological sites with poorly sorted, low densities of artefacts will be found in these places12. Defining the appropriate scale will be the key to testing our model, since we have demonstrated that broad biogeographic units are heterogenous and yet at a fine-grained scale, small areas of low suitability, which are often a local geographic feature (e.g., sand dune, bare rock outcrop, or erosional area), need not have been obstacles. Model testing will need an intermediate scale commensurate with daily foraging radii.Figure 5Boxplot of mean NDVI values and one standard deviation for low, moderate, and high-ranked suitability classes for the eleven largest IBRA subregions (a–k) of the Western Desert (l). Mean NDVI for individual bioregions and the spatial bounds of the Western Desert study area denoted as dashed black line and solid green line, respectively. IBRA subregion boxplot groups (a–k) are presented in order of increasing percentage of high-ranked foraging habitat, after Fig. 4. Table S2 offers the precise summarised NDVI values for each bioregion and suitability class.Full size imageAt present, the archaeological land use pattern of low-ranked foraging habitats is not something that is well-understood from the Western Desert, although periodic and short term use of impoverished, low productivity patches has been predicted12. Studies of contemporary Western Desert groups indicate that human-induced firing of the landscape enhances biodiversity and land productivity51,52,53,54, so it is possible that low productivity patches may have occasionally benefited from anthropogenic burning, especially in the past 1500 years51. However, research also suggests that cultural burning practices did not have widespread regional impacts51,52,53,54,55. Human influence on landscape modification is localized within day-range foraging areas around residential camps and frequently traversed pathways51,52,53,54. Low productivity patches away from residential camps were probably unlikely targets for either anthropogenic burning or foraging if higher-ranked patches were closer.Elsewhere, in the eastern Australian arid zone, periodic use of climatically harsh desert localities is known from archaeological sequences. While in some cases preservation may explain chronological discontinuities56, there is compelling evidence for irregular occupation in several desert areas10,57,58,59,60. For instance, in the western Strzelecki Desert broad portions of dunefield landscapes were periodically abandoned for centuries or even millennia57,60, while in semi-arid portions of southeastern Australia sequences of occupation were separated by decades or centuries of local/regional abandonment58,59. Fluctuations in local foraging suitability may well be a factor producing discontinuous land use across the Australian arid lands, and we suggest that in the Western Desert there were patches with chronologically varying foraging potential. The key test of this prediction would be to investigate whether archaeological sites in locations of fluctuating habitat suitability over time also display histories of discontinuous visitation. Such sites could be identified through local palaeoenvironmental records but we suggest that selections based on time-series analysis of vegetation greenness from the past few decades would be more readily used to establish samples and would facilitate comparison of archaeological sites in terms of local foraging suitability and NDVI values, as well as archaeological records of continuous or discontinuous visitation.Satellite data reveals a more nuanced understanding of land useAustralian archaeological research has relied heavily on biogeographic principles to distinguish the ‘barriers and boundaries’ of Aboriginal subsistence and settlement in the arid zone4,5,61. While equating particular land use practices with specific bioregional areas was initially useful for generalized conceptualizations of traditional foraging behaviors, the coarse analytical scale of earlier approaches is now problematic. Subsequent research has shown the dynamics of Aboriginal occupation and land usage in the Western Desert to be more complex and variable across spatial and temporal scales than originally conceived9,24,30,33. To gain a more nuanced understanding of past land use and foraging patterns, finer-scale methods of analysis are required.We used satellite imagery to tackle the issue of scale, allowing for a sharper and more spatially explicit examination of desert environments and landscapes. For example, as we focus at higher resolution on various areas of the Western Desert, our model clearly shows that foraging suitability is highly varied across all desert lowlands (Figs. 3, 4 and 6). In sandridge desert areas, proposed to have been a barrier at times in the past4, the model shows there are many well-watered and amply vegetated localities where good foraging is possible when rainfall is high and surface water is abundant (Fig. 6a). In this context, interdunal swales are hardly barriers to occupation because they can be lush with water, plant, and wildlife resources after local rain, and the energy expenditure required to walk along interdunal swales is low in comparison to the requirements needed to repeatedly scramble across a sea of loose sands and undulating dunes. Thus, it seems entirely plausible that resident groups could navigate and forage in many dunefield areas by following a well-resourced network of swales during times of good environmental conditions. The fine-grained nature of this observation opens up the possibility that many sandridge deserts were not necessarily broad barriers to occupation and that precontact land use behaviors varied in different dunefield contexts.Figure 6High resolution perspectives of various Western Desert landforms (e.g., sandridge, stony plain and sandy plain contexts) with generally higher-ranked and lower-ranked areas of foraging suitability. This figure illustrates the fine-grained scale of our habitat suitability model (Fig. 3), which has implications for better understanding localized land use behaviors. Juxtaposed areas, as mapped in Fig. 3 are: (a) Higher-ranked sandridge habitats vs. (b) lower-ranked sandridge land system. (c) Higher-ranked stony desert habitat vs. (d) lower-ranked stony desert areas. (e) Higher-ranked sandplain land systems vs. (f) lower-ranked plain habitats. Maps created in ESRI ArcGIS Desktop 10.5.1 (https://desktop.arcgis.com), linear stretch (1.0%) visualization. See “Methods” section for source raster information.Full size imageWe also highlight that the resource-rich swale pattern is not found in all dune systems (Fig. 6b), and it is plausible that some of these areas were periodic barriers to occupation, as previously suggested in more generalized ecological models4. There are substantial areas of sandridge desert, especially within central areas of the Western Desert (e.g., GID02), where survival would have always been extremely difficult, even during times of abundance (Fig. 6b). This variability is also expressed in stony desert contexts, where southern areas of the lateritic Gibson Desert (GID01) offer better habitat suitability (Fig. 6c) than the northern areas (Fig. 6d). On a fine scale, plain land systems also exhibit a wide range of habitat suitability, where high-ranked habitat suitability appears fairly widespread in some areas of the Nullabor Plain (NUL01; Fig. 6e), yet other areas of the plain were poorly-suited for foraging (Fig. 6f).In previous ecological models, stony desert and plain land systems are considered more favorable than sandridge desert4; however, as shown above, the modelled data clearly illustrate that there are substantial areas of plains and stony desert landscapes that vary considerably between high and low-ranked habitat suitability (Figs. 3, 4 and 6). The fine-grained scale of our model adds to a growing body of research5,9,24,30,33 that demonstrates how previous pan-continental characterizations of deserts as ‘corridors’ and ‘barriers’ for foragers oversimplify the link between human behavior and biogeography. When scrutinized at high resolution, extremely unsuitable foraging and very well-suited foraging areas can potentially occur in any area of the Western Desert, regardless of the biogeography or other physical characteristics. Thus, fine-grained ecological models allow for a more nuanced and spatially-explicit understanding of the past land use behaviors that led to the formation the desert archaeological record.Using environmental remote sensing to infer LGM habitat suitabilityThere is no doubt that the Western Desert environment has changed and evolved over time, through both natural and human-induced processes8,36,37,51. The region has undergone considerable environmental fluctuations over time, resulting in landform transformations (dune aggradation, in particular) and changes in vegetation cover. The long-term physical impact of these environmental changes clearly place limitations on how modern satellite data can be used to interpret deep-time patterns of occupation and land use. However, our model shows the likely distribution of low-ranked foraging habitats when climatic conditions were much drier than present.Ethnoarchaeological accounts depict resident populations as being low density and highly mobile, frequently moving and foraging across vast expanses of territory, thereby necessitating intermittent patterns of settlement1,2,3,4. Such a mobile strategy means that large swathes of the desert could not have been continuously occupied. Our habitat suitability model (Fig. 3) makes sense of the impermanent and mobile land use strategy seen historically. For example, we document several massive areas of the Western Desert where, in combination, surface terrains are physically challenging, the nearest proximity to surface water is greater than 2 days walk, and vegetation cover, density and condition is substandard, even in the best of documented environmental conditions! These exceptionally large areas were poorly-suited to foraging, and could not have been permanently occupied in the historical period. They would only have been visited rarely, perhaps only in atypical short-term climatic events, and may even have constrained forger movement between more favorable parts of their territories. Given current palaeoclimatic evidence we infer that in the Pleistocene these low ranked habitats would have been even more inhospitable to foragers than in recent times. During the LGM, the resource yields in such areas would have been more diminished than present, making conditions for survival even more difficult than today. Consequently, we predict that unless radically different economic strategies were being employed in the Pleistocene those areas would have been only rarely visited since the peak of the last glacial cycle, ~ 24–18 ka, even though adjacent desert areas may have supported regular or at least sporadic visitation.Our hypothesis is clear, detailed, and framed to be testable by archaeological fieldwork. The number of Western Desert sites with old archaeological sequences is growing, but the sample is small, site distribution is widely scattered, and none are located in the harsher core areas identified in this study (Figs. 1 and 3). Thus, it is evident that archaeological fieldwork in those impoverished landscapes as well as environmentally richer and more reliable landscapes is necessary to understand historical land use patterns and to make statements about earlier phases of regional occupation. Our work highlights how future models of forager land use across Australia’s desert regions can comprehend the environmental complexity and fine scale of resource variability in these vast, remote and diverse places. More

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IACUC approval: all work described below was approved by the Boise State Institutional Animal Care and Use Committee: AC15-021.Site layoutWe selected 20 sites, across five drainages, within the Pioneer Mountains of Idaho—matched for elevation and riparian habitat. We split these 20 sites into 10 noise playback sites, and 10 control sites (Fig. 1A; S1). The control sites ranged from quiet, slow-moving streams to relatively loud whitewater torrents. Noise playback sites, on the other hand, were relatively quiet (not whitewater) sites, where we broadcast loud whitewater river recordings with speaker arrays hung from towers (Fig. S1; S2; S3; S4; see supplementary information for more details on noise file creation, playback equipment, and experimental setup). At five of the noise playback sites we broadcast normal river noise (hereafter referred to as ‘river noise’ sites), and at the other five noise sites we broadcast spectrally-altered river recordings (hereafter referred to as “shifted noise” sites).Our field sites were oriented along the riparian zone, with data collection occurring at three primary locations within each site (Fig. S1): (1) roughly in the middle of the speaker tower systems, (2) at a shorter distance from the middle location (mean: 198.2 ± 54.5 m SD; range: 117.6–384.5 m), and (3) and a longer distance from the middle location (in the opposite direction from the nearer location; mean: 312.7 ± 64.7 m SD; range: 249.1–479.6 m). Thus, sites were approximately 510.9 ± 98.3 m long (range: 374.7–850.6 m), along the riparian corridor. All control sites were, at minimum, 1 km apart along the riparian corridor from any noise site, to maintain acoustic independence (see Fig. 1A; S1).Data collectionBirds
We conducted three-minute avian point counts between one half hour before sunrise and 6 h after sunrise (roughly 0530–1130 h). During the project, we conducted 1330 point-counts from 28 May to 20 July 2017 and 1639 point-count events occurred from 7 May to 24 July in 2018.
Caterpillar deploymentWe deployed a total of 720 clay caterpillars throughout the 2018 breeding season. Forty caterpillars were glued to stems and branches of trees between 1 and 2.5 m high at each site (Fig. S8). Twenty caterpillars surrounded the middle point count location at each site (a set of 10 were placed upstream, and another set of 10 were placed downstream starting from the middle ARU location), while the other twenty were at upstream and downstream sampling locations (10 each at upstream and downstream locations). We placed each caterpillar along the riparian corridor, at least 1 m apart from each other30. See Supplementary information for details on caterpillar predation scoring.Bird trait analysisWe performed a trait-based analysis to understand the mechanistic patterns of bird distributions in our study paradigm. Avian vocal frequencies and body mass were collected from Hu and Cardoso 2009, Cardoso 2014, and Francis 201516,31,32. When multiple sources contained data, the values were averaged. There were a few cases where none of those sources contained a vocal frequency or mass measurement for species of interest. Thus, representative songs were downloaded from the Macaulay Library of the Cornell Lab of Ornithology based on recording quality and geographical relevance (MacGillivray’s warbler: ML42249; dusky flycatcher: ML534684; red-naped sapsuckers: ML6956), and analyzed with Avisoft SASLab Pro to obtain a peak frequency measure. Mass measurements for these ‘missing’ birds were taken from the ‘All about birds’ webpage of the Cornell Lab of Ornithology.BatsMeasuring and identifying bat callsWe measured bat activity using Song Meter 3 (hereafter “SM3”) recording units (Wildlife Acoustics Inc., Massachusetts, USA) equipped with a single SMU (Wildlife Acoustics Inc.) ultrasonic microphone. One recording unit was used at each site and we pseudo-randomly rotated the unit between the three point-count locations so that each location was monitored for at least 21 days. We mounted microphones on metal conduit at a height of ~3 m, oriented perpendicular to the ground and facing away from the stream to optimize recording conditions (Fig. S9; S10; see Supplementary information for more information).Robotic insectsWe used a modified version of Lazure and Fenton’s26 apparatus to present bats with a fluttering target (Fig. S12). This consisted of a 3 cm2 piece of masking tape affixed to a metal rod [30.48 cm length × 3.25 mm diameter], which itself was connected to a 12-volt brushed DC motor (AndyMark 9015 12 V, AndyMark Inc., Kokomo, IN, USA). The no-load revolution speed of these motors (267 Hz) falls within the range of wingbeat frequency measured in Chironomidae27,33, a group that is an important food source for many North American bat species34.We attached each motor to a tripod made of PVC piping and positioned the tripod such that the target was approximately 1.2 m above the ground. Each motor was powered by a 12 V battery (35Ah AGM; DURA12-35C, Duracell) which was controlled by a programmable 12 V timer (CN101, FAVOLCANO) to automatically start and stop the motor each night. The rotors were powered for 2 h following sunset.Prey-sound speaker playbackWe created a playlist composed of several insect acoustic cues to present gleaning bats: a beetle (Tenebrio molitor) walking on dried grass, a cricket (Acheta domesticus) walking on leaves, mealworm larvae (Tenebrio molitor) on leaves, fall field cricket (Gryllus pennsylvanicus) calls, and fork-tailed bush katydid (Scudderia furcata) calls. The cricket and katydid calls were sourced from the Macaulay Library (ML527360 and ML107505, respectively).Experimental setup for bat foraging testsMost sites received two rotors (Fig. S12) and two speakers (Fig. S13): one of each at the center of the site, and one of each at approximately 125 m from the center of the site (in opposite directions in order to have tests in a range of acoustic environments), placed roughly 10 m from the edge of the riparian zone. Rotors and speakers at the center locations were separated by at least 50 m. The exception to this setup were the four positive control (loud whitewater river) sites, which only received a single rotor and speaker separated by 50 m because of logistical difficulties of accessing those sites. We paired each rotor and speaker with an SM2BAT + bat detector equipped with an SMX-US microphone (Wildlife Acoustics Inc.)35, using tripods to elevate the microphones approximately 1 m off the ground and ~1 m from the speaker/rotor. We programmed the bat detectors with a gain of 36 dB and a trigger level of 18 dB to limit recordings to bats that were passing within the immediate vicinity. To allow for a comparison of activity between speakers and rotors, bat activity was only considered for the first two hours following sunset.Bat trait analysisWe collected bat foraging behavior and peak echolocation frequency information to use as predictors in a phylogenetically controlled trait analysis (Tables S8; S13). We based our behavioral foraging classifications on the categories of Ratcliffe et al.36 and followed the classifications of Gordon et al.37 where possible, and others38,39,40,41,42,43 where necessary. We extracted peak echolocation frequency from the 2017 and 2018 SM3 field recordings and employed two controls to decrease variability in call parameters potentially introduced via this method. First, we selected only recordings made on control sites in 2017 and 2018 (n = 740,848 calls), as echolocation call characteristics may be affected by local acoustic environments (e.g., Bunkley et al.)22. Secondly, we averaged all call parameters per species per hour at each site to decrease the possible effects of few individuals driving measurements. This resulted in 9538 species-hours of recordings, which themselves were averaged per species (Table S13).Quantifying environmental variablesWe used long-term monitoring of the acoustic environment (via Roland R05 recorders) to calculate daily sound pressure level (L50 dBA) and median frequency (kHz) values for each location (see supplementary information for details on quantification of all predictor variables).Sound pressure level (SPL)We converted 106,769 h of long-term ARU recordings into daily-averaged median sound pressure levels (L50; measured as dBA rel. 20 µPa) see refs. 13,44 using custom software ‘AUDIO2NVSPL’ and ‘Acoustic Monitoring Toolbox’ (Damon Joyce, Natural Sounds and Night Skies Division, National Park Service).Acoustic environment spectrumWe used custom software45 in the programming language R and the package ‘FFmpeg’ in command prompt to convert 106,769 h of long-term recordings into 71,282 individual 3-minute files starting each hour of the day (Fig. S5). Thus 24, 3-min files were created per acoustic recording location per day (one for every hour). We then used the packages “tuneR” and “seewave” to read in and measure the median frequency of sound files, respectively45,46,47. These hourly metrics were then averaged by date to create a daily metric.StatisticsAll models of abundance, activity, and foraging transects were generalized linear mixed effects models (glmm) in R48 using the package ‘lme4’49,50 or ‘glmmTMB’51. All distribution families were selected based on theoretical sampling processes of the data, models were checked for collinearity (VIF scores)52, and model fits were visually checked with residual plots (see supplemental R code)53.Bird abundance and bat activity
Model predictors and covariates
Both bird and bat models had the following variables in a glmm: site and bird/bat species were random effects terms and sound pressure level (dBA L50), sound spectrum (median frequency), the interaction between sound pressure level and spectrum, elevation, percent riparian vegetation, ordinal date (and a quadratic version of this), and year as fixed effects. While year is sometimes used as a random-effect term, it is suggested to be used as a fixed effect if fewer than five levels exist for that factor, as variance estimates become imprecise54,55. Additionally, moon phase was a fixed effect in the bat models56, while spectral overlap (the absolute difference between sound spectrum and bird species vocalization frequencies) and the interaction between sound pressure level and spectral overlap were fixed effects in bird models.
We attempted to fit both sound pressure level and spectrum as having random slopes for each species, yet both bat and bird models would not converge with such complex model structure. Thus, we followed group models with individual species models (see Supplementary information).

Model family distribution and link function
For both bird and bat counts, we used a negative binomial distribution with a log link, rather than a Poisson distribution, because data were over-dispersed. We plotted variance-mean relationships and residuals of multiple models to select the appropriate variance structure, and compared these with AIC to select the best-fitting distribution (see R script for further justification of these methods)54.

Individual species models
Individual species models were parameterized the same as above (except without the species term). All 12 bat species (see Tables S6; S10) and 26 of the most common birds (see Tables S2; S9) were modeled individually to be able to interpret model parameter estimates, with complex interactions, for each species.
Clay caterpillar predationWe modeled caterpillar predation with a glmm (binomial family; logit link function), using the number of individual scorers as weights in the model. Like the bird abundance model, we used site as a random effect and sound pressure level (dBA L50), spectral frequency (median), elevation, percent riparian vegetation, ordinal date, and year as fixed effects (Table S4). Additionally, the predicted number of birds at a site were modeled as fixed effects to control for varying amounts of foraging birds on the landscape.Robotic moths and prey-sound speakersRobotic moth and prey-sound speaker models were parameterized exactly the same as the overall bat activity model. That is, the model was fit with a negative binomial family (log link) with site and species as random effects and sound pressure level (dBA L50), sound spectrum (median frequency), the interaction between sound pressure level and spectrum, moon phase, elevation, percent riparian vegetation, ordinal date (and a quadratic version of this), and year as fixed effects. Additionally, the predicted number of bats at a site were modeled as fixed effects to control for varying amounts of foraging bats on the landscape.Trait analysesWe performed trait analyses with phylogenetic generalized least squares (PGLS) to control for relatedness while predicting species responses to noise12. We performed PGLS analyses with the gls function in the R package nlme57, and accounted for error in the response variable with a fixed-variance weighting function of one divided by the square root of the standard error of the response estimate58,59. We accounted for phylogenetic structure by estimating Pagel’s λ60. When λ estimates fell outside of the zero to 1 range, we fixed λ at the nearest boundary. For bird models, we used a pruned consensus tree from a recent class-wide phylogeny61. For bats, we used a pruned mammalian tree62. We used initial global models with all traits as variables that explained the responses to sound pressure level (SPL; birds and bats), spectral overlap with birdsong (birds), background frequency (bats), and the interaction between SPL and each measure of frequency (birds and bats). We then used AIC model selection63 to choose top models in explaining these patterns. Models with dAIC ≤4 are included in Table S3 (birds) and Table S8 (bats), and the top model is interpreted in the main text.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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