2. Material &
Methods
Conservation biology is a broad discipline encompassing different
thematics. For the selection of articles, we used conservation terms
that could easily be placed under the umbrella of two classification
systems as keywords: the broader CBD threat classification system, and
the more specific International Union for the Conservation of Nature
threat (Table 1) and conservation measures (Table 2) classification
systems (Annex A). Additionally, we were also looking for papers that
focused on monitoring, as we felt that, although not specifically
included in the original categories, automation of species
identification, remote sensing through camera trapping, etc. are methods
that show major growth potential in machine learning usage. Extensive
consideration was put into what terms to use in the search string to
strike a balance between sensitivity and specificity. As per the
definition of ML stated above (see section 1.), our focus was on methods
popularised recently (Table 3), with those more traditionally associated
with statistics (e.g. linear regression in its most basic forms) not
being considered.
2.1 Systematic search
A standardised literature search was performed in Clarivate Analytics’Web of Science on 8 June 2021 in Helsinki, Finland, using the
Mozilla Firefox web browser running on Windows 10. The final search
string used was composed of 33 conservation terms and 23 ML terms. For
convenience, multiple searches were made, each containing only one
conservation term combined with every machine learning term.
Effectively, our search string was: