Biodiversity monitoring is critical for conservation, useful in warning of impending extinction crises, and has direct implications for management practices for improved biodiversity targets (Noss 1990; Pereira and Cooper 2006; Lindenmayer et al. 2012). The loss of funding, logistical constraints (e.g., time and spatial scale), and lack of interest by some government authorities in fully monitoring biodiversity make it important for other methods of biodiversity monitoring to be explored. For instance, citizen science (Silvertown 2009; Dickinson et al. 2012) is currently recognized as a method for achieving broad-scale biodiversity monitoring (Pocock et al. 2018; Callaghan et al. 2019). Citizen scientists are helping to assess various ecological and biodiversity aspects of birds (Sullivan et al. 2009), coral (Marshall et al. 2012), sharks (Vianna et al. 2014), and bees (Domroese and Johnson 2017), among other taxa. Additionally, some large-scale programs, such as iNaturalist (iNaturalist.org 2018) span various taxa.

Frogs and other amphibians are sensitive to changes in their environment due to their biphasic lifestyle (with most species having an aquatic larval stage and a terrestrial adult), semi-permeable skin, and reliance on specific environmental conditions for reproduction (Hopkins 2007; Lemckert and Penman 2012). Almost one-third of the 7,000 frog species known are at risk of extinction (Stuart et al. 2014; IUCN 2019), largely due to anthropogenic threats such as habitat loss and modification, disease, and invasive species. The implications are far-reaching, with frog populations declines shown to have large-scale, long-term ecosystem-level effects (e.g., Whiles et al. 2013).

Despite the need for biodiversity data on frogs, frogs are inherently difficult to survey, leaving a lack of detailed knowledge of broad-scale distributions, occurrences, and habitat associations. This is largely a result of logistical constraints, including a lack of funding available for surveys and access to often remote sites, and the fact that many frog species are difficult to detect, having activity patterns highly reliant on weather. Many frog species are also small and camouflaged, rendering them difficult to visually locate.

The frog advertisement call serves as a premating isolation mechanism (Blair 1964; Littlejohn 1969) and is therefore typically highly species-specific. As a result, advertisement calls are often used for frog species identification during surveys (Heyer et al. 2014) and in delineating species, including the description of new species (Littlejohn 1969; Rowley et al. 2016; Köhler et al. 2017). The identification of frog species via their advertisement calls may also minimise disturbances to the frog and its habitat.

All known frog species in Australia have audible advertisement calls and only a few are difficult to identify to species via their calls alone (e.g., several species of the genus Pseudophryne Fitzinger, 1843 in the places where they co-occur; Pengilley 1971). Further, several Australian frog species that are morphologically indistinguishable from related species can be identified to species by their calls (e.g., Litoria jungguy Donnellan & Mahony, 2004 and Litoria myola Hoskin, 2007). Although female frogs have been demonstrated to call in a handful of species (e.g., Goyes Vallejos et al. 2017), only male frogs are known to produce advertisement calls in Australia.

Launched on 10 November 2017 and led by the Australian Museum, FrogID is the first citizen science initiative aimed at capturing validated biodiversity data on Australian frogs on a national scale (Rowley et al. 2019). The FrogID project collects data via a smartphone application allowing participants to submit recordings of calling frogs, which are then identified to species by experts (Rowley et al. 2019). If no frogs are heard calling (i.e., a FrogID user recorded an insect), submissions are identified as “Not a Frog”. If the recording is not sufficient to identify species (i.e., too short in duration, too much other noise in the recording), or there is an otherwise high level of uncertainty, the submission is identified as “Unidentified Frog”.

Publishing biodiversity data advances our collective knowledge on global biodiversity (Costello et al. 2013) and our ability to make informed conservation decisions. We hope that by making these occurrence data openly accessible (Michener 2015), others will find it useful, ultimately contributing to increased knowledge of Australia’s frogs and translating into increased conservation action. In this data paper, we detail the associated dataset.

Funding for the FrogID project was provided by the Australian Government’s Citizen Science Grants program, the Impact Grants program of IBM Australia provided the resources to build the FrogID App. In-kind funding was provided by the Australian Museum. Bunnings and Fyna Foods are project partners.

While effective conservation relies on accurate knowledge of where species occur, releasing the locations of observation records may have inadvertent negative impacts (Lindenmayer and Scheele 2017). Open locality information has resulted in the poaching of wildlife (Stuart et al. 2006), and particularly in the age of social media, access to precise locality data for certain species may also drive enthusiasts or wildlife photographers to locate, photograph or even remove species, sometimes resulting in habitat disturbance (Lindenmayer and Scheele 2017; Pike et al. 2010; Tulloch et al. 2018). A consideration of the potential impacts of publishing exact locality information is likely to be particularly important for FrogID records for three reasons: (1) FrogID occurrence data are derived from recordings of male frogs calling at breeding habitats, and habitat disturbance at these vital locations may influence breeding success; (2) visually locating or photographing frogs may disturb both the frog and breeding habitat, particularly for species that call from concealed microhabitats such as burrows (e.g. Pseudophryne and Philoria species); and (3) one of the major threats to frog species is disease, and pathogens may be transferred between individual frogs and between sites by people, representing a real risk to many frog species. For threatened frog species, or frog species with highly restricted distributions, revealing exact FrogID localities may therefore have serious, unintended negative consequences. Revealing exact localities for such species on private land may also result in trespassing (Lindenmayer and Scheele 2017).

We therefore follow ethical data publication guidelines (e.g., Chapman and Grafton 2008) and consider certain records as sensitive, thereby reducing geolocation accuracy in our publicly available dataset. We implement three geoprivacy options (Table 1) that take into account the state and national (DEE 2019) threat listings of the species, whether the species is range-restricted (i.e., has a geographic range or extent of occurrence of <20,000 km²), and whether the record falls within the known geographic range of these species (Table 2; Suppl. material 1). Further, because we provide the user id, the call id, and the time of every submission, for any submission which included either an obscured or private species, all species recorded in that submission also received the higher geoprivacy options. This means, for example, that some records of common and ‘open’ species are obscured. A total of 1,504 records’ coordinates for 74 species were therefore rounded to 0.1 degrees in this dataset. The complete dataset including sensitive information will be made available under licence to specific organisations and can be requested from the FrogID project.

Kingdom: Animal

Phylum: Chordata

Class: Amphibia

Order: Anura

Families: Bufonidae, Limnodynastidae, Microhylidae, Myobatrachidae, Pelodryadidae, Ranidae

Genera: Adelotus, Assa, Austrochaperina, Cophixalus, Crinia, Cyclorana, Geocrinia, Heleioporus, Lechriodus, Limnodynastes, Litoria, Metacrinia, Mixophyes, Myobatrachus, Neobatrachus, Notaden, Papurana, Paracrinia, Philoria, Platyplectrum, Pseudophryne, Rhinella, Uperoleia

Species: Adelotus brevis, Assa darlingtoni, Austrochaperina adelphe, Austrochaperina fryi, Austrochaperina gracilipes, Austrochaperina pluvialis, Austrochaperina robusta, Cophixalus australis, Cophixalus bombiens, Cophixalus infacetus, Cophixalus ornatus, Cophixalus saxatilis, Crinia bilingua, Crinia deserticola, Crinia flindersensis, Crinia georgiana, Crinia glauerti, Crinia insignifera, Crinia parinsignifera, Crinia pseudinsignifera, Crinia remota, Crinia signifera, Crinia sloanei, Crinia subinsignifera, Crinia tasmaniensis, Crinia tinnula, Cyclorana alboguttata, Cyclorana australis, Cyclorana brevipes, Cyclorana cultripes, Cyclorana longipes, Cyclorana maculosa, Cyclorana maini, Cyclorana novaehollandiae, Cyclorana occidentalis, Cyclorana platycephala, Cyclorana verrucosa, Geocrinia laevis, Geocrinia leai, Geocrinia rosea, Geocrinia victoriana, Heleioporus albopunctatus, Heleioporus australiacus, Heleioporus barycragus, Heleioporus eyrei, Heleioporus inornatus, Heleioporus psammophilus, Lechriodus fletcheri, Limnodynastes convexiusculus, Limnodynastes depressus, Limnodynastes dorsalis, Limnodynastes dumerilii, Limnodynastes fletcheri, Limnodynastes interioris, Limnodynastes peronii, Limnodynastes salmini, Limnodynastes tasmaniensis, Limnodynastes terraereginae, Litoria adelaidensis, Litoria aurea, Litoria barringtonensis, Litoria bicolor, Litoria brevipalmata, Litoria burrowsae, Litoria caerulea, Litoria chloris, Litoria citropa, Litoria cooloolensis, Litoria coplandi, Litoria cyclorhyncha, Litoria daviesae, Litoria dayi, Litoria dentata, Litoria electrica, Litoria eucnemis, Litoria ewingii, Litoria fallax, Litoria freycineti, Litoria gilleni, Litoria gracilenta, Litoria inermis, Litoria infrafrenata, Litoria jervisiensis, Litoria jungguy, Litoria latopalmata, Litoria lesueuri, Litoria littlejohni, Litoria meiriana, Litoria microbelos, Litoria moorei, Litoria nasuta, Litoria nigrofrenata, Litoria nudidigitus, Litoria olongburensis, Litoria pallida, Litoria paraewingi, Litoria pearsoniana, Litoria peronii, Litoria personata, Litoria phyllochroa, Litoria raniformis, Litoria revelata, Litoria rheocola, Litoria rothii, Litoria rubella, Litoria serrata, Litoria subglandulosa, Litoria tornieri, Litoria tyleri, Litoria verreauxii, Litoria watjulumensis, Litoria wilcoxii, Litoria xanthomera, Metacrinia nichollsi, Mixophyes balbus, Mixophyes carbinensis, Mixophyes coggeri, Mixophyes fasciolatus, Mixophyes fleayi, Mixophyes iteratus, Mixophyes schevilli, Myobatrachus gouldii, Neobatrachus aquilonius, Neobatrachus kunapalari, Neobatrachus pelobatoides, Neobatrachus pictus, Neobatrachus sudellae, Neobatrachus sutor, Neobatrachus wilsmorei, Notaden bennettii, Notaden melanoscaphus, Notaden nichollsi, Papurana daemeli, Paracrinia haswelli, Philoria kundagungan, Philoria loveridgei, Philoria pughi, Philoria richmondensis, Philoria sphagnicola, Platyplectrum ornatum, Platyplectrum spenceri, Pseudophryne australis, Pseudophryne bibronii, Pseudophryne coriacea, Pseudophryne dendyi, Pseudophryne douglasi, Pseudophryne guentheri, Pseudophryne major, Pseudophryne occidentalis, Pseudophryne raveni, Pseudophryne semimarmorata, Rhinella marina, Uperoleia altissima, Uperoleia arenicola, Uperoleia aspera, Uperoleia borealis, Uperoleia crassa, Uperoleia daviesae, Uperoleia fusca, Uperoleia inundata, Uperoleia laevigata, Uperoleia lithomoda, Uperoleia littlejohni, Uperoleia mahonyi, Uperoleia mimula, Uperoleia minima, Uperoleia mjobergii, Uperoleia rugosa, Uperoleia saxatilis, Uperoleia talpa, Uperoleia trachyderma, Uperoleia tyleri.

Object name: FrogID dataset

Character encoding: UTF-8

Format name: Darwin Core Archive Format

Format version: 1.0

Distribution: https://doi.org/10.15468/wazqft; https://zenodo.org/record/3612700

Publication date of data: 22 January 2020

Language: English

Licenses of use: Creative Commons Attribution (CC-BY) 4.0 License

Metadata language: English

Date of metadata creation: 19 January 2020

Hierarchy level: Dataset

The dataset includes basic biodiversity occurrence data, with Darwin Core terms (http://rs.tdwg.org/dwc/terms/), and is summarized in Table 3.