Geodiversity inclusiveness in biodiversity assessment

Abstract

Biodiversity assessment is constitutive in establishing conservation priorities and outcomes, and geodiversity complements species richness as a surrogate in the absence of species data, improves statistical modelling and can facilitate prediction of species distribution and abundance. Yet, geodiversity is frequently excluded, and biodiversity prioritised in conservation endeavours such as ecosystem-based management. Therefore, combined geodiversity and biodiversity assessment approaches present practical benefits to conservation such as improved collaboration between biologists and geoscientists, efficacious indicators of conservation value, and abatement of biodiversity partialities and wider inclusion of geodiversity in conservation literature. This study scientometrically analysed 240 biodiversity assessment publications to investigate geodiversity inclusiveness, methodological trends, geographic trends, environment-type trends and future directions in biodiversity assessment methods. Results showed these species richness articles frequently included geodiversity-relevant terms such as hydrological, soil, geological and geomorphological components, but the all-encompassing ‘geodiversity’ term was absent entirely. Geographic trends showed many potential economic, social, cultural and political factors influencing geodiversity inclusiveness in biodiversity assessment. For example, Australia’s relatively resource exploitative approach to geology and early involvement in the inception of the geodiversity concept could explain the high frequency of geological-related terms in Australian biodiversity assessments. Methodological trends showed dominance by field-based biodiversity assessments such as trapping methods, followed transects, quadrats, net methods and observations. Given the specific sample size of literature analysed, inferences from this study relate only to biodiversity assessment methods and not biodiversity discourse in its entirety. Subsequent research could investigate specific factors, such as social, economic or political, and their influence on geodiversity inclusiveness in biodiversity assessment methods.

Keywords

scientometric geodiversity biodiversity assessment species richness evolution

1. Introduction

1.1 Geodiversity inclusiveness in biodiversity assessment and conservation outcomes

Geodiversity is the ‘natural range (diversity) of geological (rocks, minerals, fossils), geomorphological (landforms, topography, physical processes), soil and hydrological features’ (Gray, 2013), and geodiversity assessment is the evaluation of geodiversity using qualitative or quantitative methods such as the Serrano and Ruiz-Flano (2007) geodiversity index (Forte et al., 2018). Biodiversity is the variety of genes, species and ecosystems that make up life on Earth (Lausch et al., 2016; Noss, 1990; Pomerantz et al., 2018; Reddy et al., 2020). Biodiversity assessment is the quantification of biodiversity – most commonly represented as species richness (Ahrendsen et al., 2016) – using methods such as remote sensing (Mohapatra et al., 2019; Reddy et al., 2020; Wu and Liang, 2018), molecular techniques (Avó et al., 2017; Krehenwinkel et al., 2019), bioindicators (Tichit et al., 2010), bioacoustics (Gasc et al., 2015; Sueur et al., 2014), and presence or absence surveys (Eyre and Leifert, 2012).

Biodiversity assessment is the first step in facilitating biological conservation processes and outcomes (Steele and Pires, 2011). Biodiversity assessment informs models predicting degree of change, actions for conservation outcomes and priorities (Pollock et al., 2020), and provides indicators for conservation value (Pauchard et al., 2018). Conservation priority is attributed to areas exhibiting high species richness (Fleishman et al., 2006). Similarly, geodiversity inventorying – a qualitative approach to geodiversity assessment – is the first step in geoconservation strategies (Brilha, 2015). Crisp et al. (2022a) suggested that a geodiversity value, similar to species richness, could be used as a proxy for geoconservation values and exigencies. However, the gargantuan task of assessing species globally (Webb et al., 2010; Costello et al., 2013) presents many challenges for assessment methods such as scale (Chiarucci et al., 2011), time and budget constraints (Miller, 2007; Carbayo and Marques, 2011; Yu et al., 2012; Gasc et al., 2015), and extensive taxonomic expertise requirements (Yu et al., 2012). These limitations can lead to under-representation of threatened species in global databases, inventories and distributional models (García, 2006; Choe et al., 2016; Frota et al., 2016), and hindered conservation outcomes (Faith and Walker, 1996; Howard et al., 2000; Van Gemerden et al., 2005).

The value of geodiversity obtained from a geodiversity assessment can be used as a surrogate in the absence of species data (Anderson et al., 2015), improve biodiversity modelling (Hjort et al., 2012; Bailey et al., 2017, 2018; Tukiainen et al., 2017), and predict species distribution and abundance (Toivanen et al., 2019). Further, geodiversity and biodiversity are intrinsically linked (Santos et al., 2017; Santucci, 2005) and geodiversity underpins biodiversity and contributes to the capacity of an ecosystem to harbour life (Antonelli et al., 2018; Crisp et al., 2022b). The assessment of geodiversity can provide greater insight into an ecosystem’s potential to harbour species diversity (Anderson and Ferree, 2010; Lawler et al., 2015; Toivanen et al., 2019). However, the intrinsic relationship between geodiversity and biodiversity does not indicate that areas of high geodiversity harbour higher biodiversity, with some instances of low geodiversity areas exhibiting significantly high biodiversity (Santos et al., 2017; Santucci, 2005), such as some low relief areas of the Amazon rainforest (de Paula Silva et al., 2021; Val et al., 2022). However, the literature indicates that dimensions of geodiversity are related to biodiversity and contribute to the capacity of a habitat to support biodiversity (Santos et al., 2017; Santucci, 2005), and that the conservation of geodiversity contributes positively to enhanced biological conservation outcomes (Gray, 2018). Yet, geodiversity and biodiversity are considered to be separate entities (Matthews, 2014), and geodiversity is frequently excluded from conservation literature such as ecosystem-based management (Gray, 2018). For example, only 12% of geodiversity assessment scholars considered biodiversity in their methodological intentions (Crisp et al., 2021), and scholars have been calling for increased collaboration between biologists and geoscientists (Chakraborty and Gray, 2020) given the integral role biodiversity (Fleishman et al., 2006; Steele and Pires, 2011; Pauchard et al., 2018; Pollock et al., 2020) and geodiversity (Gray, 2018; Toivanen et al., 2019; Crisp et al., 2021, 2022a) have in facilitating conservation outcomes.

Combined biodiversity and geodiversity assessment approaches would encourage greater collaboration between biologists and geoscientists (Chakraborty and Gray, 2020), facilitate a more balanced treatment of both biodiversity and geodiversity in conservation literature, and lead to wider dissemination and inclusion of geodiversity in conservation processes and outcomes. There are many practical benefits to combined biodiversity and geodiversity assessment approaches in conservation (Toivanen et al., 2019). For example, given the intrinsic relationship between biodiversity and geodiversity (Santucci, 2005; Santos et al., 2017) and the use of assessment approaches to provide indicators for conservation value (Pauchard et al., 2018), a combined approach could provide efficacious indicators to effectively manage conservation priorities and outcomes, facilitate a streamlined approach to assess natural diversity in ensuing conservation processes such as ecosystem-based management and geoconservation (Crisp et al., 2022a), add power to statistical models (Hjort et al., 2012; Bailey et al., 2017, 2018), and geodiversity value a surrogate of biodiversity (Anderson et al., 2015). For example, Crisp et al. (2022b) developed a novel concept entitled Omnidiversity, which combines both geodiversity and biodiversity assessment in a geoconservation context; this study demonstrated that consolidation of both concepts enhanced both biodiversity and geodiversity monitoring, facilitated a more time and cost-effective approach to holistically assess and conserve geodiversity and biodiversity, and overall found that consolidated approaches can help facilitate effective assessment and management of vulnerable environments. Scholars have been calling for geodiversity assessments in biodiversity hotspots such as World Natural Heritage Sites to determine key geological and geomorphological drivers of biodiversity in conservation areas (Chakraborty and Gray, 2020). Therefore, given the increased recognition of the harbouring potential of geodiversity for biodiversity (Anderson and Ferree, 2010; Lawler et al., 2015; Toivanen et al., 2019), the constitutive role of assessment approaches in conservation endeavours (Steele and Pires, 2011; Pauchard et al., 2018; Crisp et al., 2022a, 2022b), and the need for geodiversity assessments in biodiversity conservation areas (Gray, 2018; Chakraborty and Gray, 2020), a combined approach to geodiversity and biodiversity assessment is a viable and useful direction of subsequent research.

Crisp et al. (2021) scientometrically analysed geodiversity assessment literature to determine the degree to which geodiversity assessment scholars have included biodiversity assessment in their methodological intentions and found that only 12% of articles strongly linked biodiversity to geodiversity assessment. Few methodological tools are developed to combine geodiversity and biodiversity assessment, particularly in a conservation context (Crisp et al., 2022b), with current literature using statistical tools to link elements of geodiversity to trends in biodiversity. A similar scientometric approach to determine geodiversity inclusiveness in biodiversity assessment would complement Crisp et al. (2021) and potentially provide insight into useful future directions for scholars to facilitate and develop new methods to consolidate biodiversity and geodiversity assessment in a conservation context, and alleviate partialities towards biodiversity in conservation literature.

1.2 Objectives

This study determines geodiversity inclusiveness in biodiversity assessments with focus on species richness, to determine the degree to which biodiversity assessment scholars have included geodiversity assessment in their research scope. The study adapts the scientometric methodology of Crisp et al. (2021), which analysed the reverse, of biodiversity inclusiveness in quantitative geodiversity assessment methods, and allows direct comparison of results. This study scientometrically analyses a representative sample of 240 biodiversity assessment publications for degree of geodiversity inclusiveness, methodological trends, geographic trends in geodiversity inclusiveness, environment-type trends and suggestions for future directions in biodiversity assessment methods. This study comprises three areas of analysis: (1) Geodiversity inclusiveness in biodiversity assessment to determine the level that scholars have considered geodiversity in their assessments, using a trending value formula from Crisp et al. (2021), (2) Methodological trends to analyse common approaches used to assess species richness biodiversity and (3) Geographic and environment type trends to map coordinates of study sites from sourced articles and colour-code each study site geographic point with trends in geodiversity inclusiveness and environment type used.

2. Materials and methods

2.1 Sourcing of a representative sample of publications and final output

The queries executed in Scopus, Web of Science and Google Scholar (Table 1) were used to source a representative sample of biodiversity assessment publications from the years 2000 to 2021. A representative sample was sourced given wide dissemination of the term and contexts of ‘biodiversity’ in scientific literature; in Google Scholar ‘biodiversity’ returned 2.9 million publications and ‘biodiversity assessment’ 2.46 million. Implementation of the trending value (Crisp et al., 2021) and investigation of methodological trends in the above returned articles would be a gargantuan task requiring significant input and time from numerous scholars. Terms were selected to focus on species richness and diversity of species (Table 1). To return a representative and practicable sample for analysis, restrictive search queries were applied to the ‘titles’ of articles (Table 1); hence, inferences in this study are generalised to represent trends in biodiversity assessment methods considered, and not biodiversity as whole, based on the very specific articles returned. A topic search in the abstract or body of articles returned an abundance of literature not specific to biodiversity assessment methods.

Table 1.

The keyword search queries executed in Google Scholar, Web of Science and Scopus. In addition, the bibliographic or reference list search was applied.

Database name	Keyword search query (including Booleans)
Scopus	TITLE (‘biodiversity assessment’) OR TITLE (‘biodiversity assessment method’) OR TITLE (‘biodiversity indices’) OR TITLE (‘biodiversity richness assessment’) OR TITLE (‘biodiversity abundance assessment’) OR TITLE (‘biodiversity method’)
Web of Science	TI = (biodiversity AND assessment) AND TS= (biodiversity AND assessment AND diversity AND species AND diversity)
Google Scholar	Allintitle: Surveys OR rapid OR species OR diversity OR terrestrial OR data OR richness OR index OR DNA OR marine ‘biodiversity assessment’ -predicting -impact -conservation -monitoring -value -values
Bibliography or reference list search	The following words were searched for using the text search query tool in NVivo: ‘biodiversity’, ‘biotic’, ‘species’, ‘richness’, or ‘assessment’

2.2 Geodiversity inclusiveness in biodiversity assessment

Crisp et al. (2021) used a novel ranking formula (hereafter, ‘trending value’) to determine the degree to which geodiversity assessment scholars included biodiversity in their methodological intentions. In this study, the same trending value approach was adapted to determine the degree to which biodiversity assessment scholars included geodiversity in their methodological intentions

\sum [\log (C 1) + x_{1} (C 2) + (\frac{C 3 \times N}{6})] - [y_{1} (C 4) + (\frac{C 5 \times N}{6}) + \log (C 6 + G B_{L})] \times \frac{[C 2]}{[C 4]}

(1)

The criteria C1, C6 and GB_L counted overall geodiversity, biodiversity and linking terminology (Table S1 and S2). X₁ and Y₁ were attributed a value based on a strength rating (Table S1). For X₁, a higher value indicated an article proposing new methods to assess biodiversity in the absence of geodiversity, and the opposite for lower values. The same was true for values attributed to Y₁ except in a geodiversity context. C2 and C4 were attributed a value based on the ranking of nine sub-criteria from 0 to 4 (Table S2). C2 and C4 measured the degree to which biodiversity and geodiversity was considered in each publication. The results from C2 and C4 were amplified using X₁ and Y₁ to overcome skewing of data from terminology counts, given the inherent variation in the number of terms used in each article; the terminology influence was further mitigated using Log₁₀ (Table S1). Therefore, the output trending value represented the level of the assessment, and not terminology counts. Criteria C3 and C5 counted relevant terms in each section of an article, and counts were weighted (N) by pre-defined values for each section (Table S2). Relevant terms considered could include a multitude of abiotic components within an environmental context, such as springs, lakes, soil, mountain, and others. However, the use of these above terms does not imply any of measure of ‘diversity’, with many terms likely using the terms to describe environmental components in general; hence, the ranking value criteria in C2 and C4 (Table S2) accounted for this by determining the objective of each publication, with component terms mentioned in articles exploring links between biodiversity and abiotic nature likely referring to some measure of geodiversity. Terms in the abstract were weighted highest by 30%, given they summarise articles and typically include the research question or objective (Jalalian, 2012). Terms in the methods also weighted highest as information on study design and parameters are provided (Liumbruno et al., 2013). The value for C3 and C5 was divided by six, the common number of sections in a typical article (abstract, introduction, methods, results, discussion and conclusion), so that the trending value was still representative of C2 and C4. The outputs were then multiplied by a ratio of the highest and lowest value from C2 or C4. This ensured the trending value reflected values obtained from the 18 questions from C2 and C4. The trending values were then normalised (Table S6), and the output heatmap figure (Figure 1) was generated in MATLAB R2020a. Normalising the values facilitated showing the distribution of methodological intentions, whereas raw trending values were more indicative of the level to which geodiversity was considered. Values closer to one (0.9–1) indicated biodiversity assessments with no links to geodiversity, values between (0.4–0.6) mentioned and discussed geodiversity-relevant terms in relation to biodiversity, and values closer to zero indicated strong practical relationships to geodiversity such as models determining the statistical relationship between geodiversity and biodiversity, or combined geodiversity and biodiversity assessment approaches (0–0.1). Rationale for the criteria and associated weightings in the trending value (Equation (1)) are provided in Crisp et al. (2021).

Figure 1.

Trending value showing to what level scholars considered geodiversity or its assessment in their methodological intentions.

2.3 Methodological and geographic trends

Using NVivo software, relevant information from articles was sourced and populated in attributes relevant to the following areas of analysis: (a) Geodiversity inclusiveness, (b) methodological trends and (c) geographic and environment-type trends. The attribute information was then exported to Excel and imported into MATLAB for analyses and figure creation (Figures 1–4; Figures S1–S3).

Figure 2.

Count of geodiversity definition components mentioned in articles based on the Gray (2013) definition, and count of mentions of geodiversity components and whether they were described as being influential over biodiversity (influential trend), and whether combined geodiversity and biodiversity assessments were implemented in the study (combined trend).

Figure 3.

Count of common environment types used in biodiversity assessments as study sites.

Figure 4.

Global distribution of biodiversity assessment study sites by each occurrence of geodiversity component terms. (a) Occurrence of ‘geology-related’ component term at study site. (b) Occurrence of ‘geomorphology-related’ component term at study site. (c) Occurrence of ‘soil-related’ component term at study site. (d) Occurrence of ‘hydrological-related’ component term at study site. (e) Occurrence of ‘all component terms’ used at study site. (f) Occurrence of whether scholars acknowledged influence of geodiversity on biodiversity at study site.

For geodiversity inclusiveness, the following attributes were populated as ‘yes or no’ (Table S3): (1) Geodiversity components mentioned in articles? The Gray (2013) definition of geodiversity comprises four components (geological, geomorphological, soil and hydrological) and this attribute facilitated counting mentions of each geodiversity component in articles or whether articles mentioned all components (all definition components). (2) Is the influence of geodiversity components mentioned? This attribute facilitated counting acknowledgements in each article of the influence of individual components of geodiversity on the value of biodiversity. (3) Does the approach combine geodiversity and biodiversity assessment? This attribute facilitated counting how many scholars combined geodiversity assessment with biodiversity assessment in their methodological intentions (Figure 2).

For methodological trends (Table S3), the following attributes were populated as ‘yes or no’ to highlight overall trends in biodiversity assessment methods: (1) Are existing methods used? (2) Is a novel approach or tool developed? and (3) are existing methods compared to facilitate or complement biodiversity assessment methods? (Table 2). Specific tools used by scholars were populated in attributes and were then categorised into field, laboratory, numerical and spatial-based approach groups to highlight common approaches used; occurrences of each approach category in articles were then counted (Table 2). The word frequency query in NVivo was used to assess the frequency of specific tools used in word cloud figures (Figures S1–S3). Field-based attribute was populated when fieldwork was included in the biodiversity assessment. Laboratory-based attribute was populated when laboratory equipment was used. The numerical-based attribute was populated when statistical tools or biodiversity indices were used, and the spatial-based attribute was populated when spatial analytical tools were used to model or show geographic distributions.

Table 2.

Responses to methodological trend attribute categories from each biodiversity assessment.

Methodological trends
Attributes to highlight overall methodological trends	Yes	No
Are existing methods used?	212	28
Is a novel approach or tool developed and implemented?	85	155
Are existing methods compared to better facilitate biodiversity assessments?	44	196
Count of common methodological approaches used biodiversity assessment
Field-based methods (e.g., species sampled in-field and analysed using numerical or spatial approaches)	221
Laboratory-based methods (e.g., species sampled from archive, field sampling in a preceding publication, or online species database and then analysed using laboratory techniques)	160
Numerical-based (e.g., using biodiversity indices or statistical analyses in conjunction with field or laboratory approaches)	198
Spatial-based (e.g., using GIS or remote sensing in conjunction with field or laboratory approaches)	49

For geographic trends, environment type was sourced from the methods of each article (Figure 3), and the latitude and longitude coordinates of research sites in articles were sourced using NVivo 12, or a Google Map search when coordinates were not provided and were mapped in MATLAB using the geobubble tool colour-coded by geodiversity inclusiveness attributes (Figure 4).

3. Results

3.1 Database outputs

An unfiltered search in Google Scholar returned 2,960,000 articles, Scopus 846,931 and Web of Science 200,510. Many articles from broad research domains were returned such as biodiversity in conservation, integration of global and local values, technical reports, land use change and others. The queries (Table 1) reduced the output frequency to 348 for Scopus, 223 for Google Scholar and 272 for Web of Science. The elimination process reduced further to 211 for Scopus, 15 for Google Scholar and 14 for Web of Science. Table S4 lists the 240 biodiversity assessment articles analysed in this study.

3.2 Geodiversity inclusiveness in biodiversity assessment

After 2015, three articles ranked above 0.7 (Bakker et al., 2019; Daly et al., 2018; Pomerantz et al., 2018), and seven articles below 0.3 (Farinha-Marques et al., 2017; Gatti and Notarnicola, 2018; Prasanna et al., 2017) (Figure 1). Before 2015, 13 articles ranked above 0.7 (Adams et al., 2014; Hammer et al., 2014; Salako et al., 2014), and 16 articles below 0.3 (Gioia and Pigott, 2000; Ponder et al., 2001; Roxanne Steele and Chris Pires, 2011). Before 2015, most articles ranked between 0.2 and 0.4 (Geburek et al., 2010; Schindler et al., 2008; Sueur et al., 2014). Conversely, most articles ranked between 0.5 and 0.7 after 2015 (Jo et al., 2019; Li, 2020; Lopes et al., 2017). The increasing trending value suggests biodiversity assessment scholars have trended away from geodiversity inclusiveness in recent years (Figure 1; Table S4).

For example, the low-ranking Khelifa (2019) method, 0.1, explored the sensitivity of biodiversity indices such as species richness to life history stages, habitat type and landscape in Odonata communities, and geodiversity inclusiveness was apparent through mentioning geomorphological and hydrological component terms. The mid-ranking method Yuanike et al. (2019), 0.6, assessed hard coral types in Indonesia and made no reference to geological, geomorphological or soil component terms; some reference to hydrological terms such as ‘ocean’ or ‘water’ attributed to its marine protected area study site and not to methodological intentions of geodiversity inclusiveness. However, biodiversity assessment was not the entire objective, with the article deviating from independent biodiversity assessment by also assessing conservation status, or the condition of coral; hence, the method was not attributed a lower or higher trending value. The high-ranking method Bakker et al. (2019), 0.9, used eDNA metabarcoding to assess the biodiversity of pelagic marine communities in coastal shelf habitats in the Caribbean sea; mention of water samples attributed to its objective to assess water samples, however, there was no reference or link to geodiversity component terms mentioned and their influence on biodiversity value (Table S6).

Geodiversity component terms from the Gray (2013) definition of geodiversity were frequently mentioned in biodiversity assessment literature (Figure 2). Most biodiversity assessments mentioned hydrological components of geodiversity such as rivers, ocean, lakes and springs (Mimouni et al., 2018; Alam et al., 2020; Nneji et al., 2020; Suárez-Tangil and Rodríguez, 2021). For example, Alam et al. (2020) assessed fish biodiversity in Korean ‘rivers’ using eDNA metabarcoding approaches. Soil components were mentioned second-most commonly (Drummond et al., 2015; Tasser et al., 2019; Nørgaard et al., 2021), followed by geological components (Coops et al., 2009; Kitching and Ashton, 2013; Lindqvist et al., 2016), and geomorphological components (Reyers et al., 2007; Mohapatra et al., 2019; Ge et al., 2021). Biodiversity assessments infrequently mentioned all definition components (Gioia and Pigott, 2000; Burke et al., 2008; Coops et al., 2009; Graham et al., 2010; Sosa et al., 2014; Gatti and Notarnicola, 2018), with most cases before 2010, supporting the above increasing trending value in recent years (Figure 1). For example, Coops et al. (2009) discussed relevant interactions between biodiversity and geodiversity component terms such as geology, landform geomorphology, soil, hydrology and climate. Although many mentioned geodiversity-relevant terms, few mentioned the influence on biodiversity (Calheiros et al., 2020; Gioia and Pigott, 2000; Gasc et al., 2013; Jones and Eggleton, 2000; Khera et al., 2001; Ponder et al., 2001; Ritter et al., 2020; Schulze et al., 2020; Wessels et al., 2000).

For example, Gasc et al. (2013) mentioned links between distributional range of species and geomorphological features such as mountains, and Ritter et al. (2020) mentioned that soil physicochemical parameters influence biodiversity dynamics and patterns. However, similar to the trend observed in geodiversity assessment literature (Crisp et al., 2021), there were no combined geodiversity and biodiversity assessments (Figure 2). Interestingly, of all 240 biodiversity assessment articles considered, none mentioned the overarching term ‘geodiversity’, even in articles where all definition components were considered.

3.3 Methodological trends

Majority of biodiversity assessments were implemented using existing methodological approaches (Table 2). For example, field-based approaches in Giordani et al. (2009) used quadrats to systematically sample lichen diversity, laboratory-based morphospecies identification, and the numerical-based approaches comprised Pearson r correlation using STATISTICA 6.0 software. Conversely, new approaches were adopted less frequently (Table 2). For example, Maslin et al. (2021) proposed and implemented an alternative field-based approach to assess and monitor fish biodiversity using semi-autonomous underwater vehicles, complementing or replacing traditional approaches like scuba diving. Further, Bayat et al. (2021) used existing field-based approaches such as diameter at breast height measurement of trees in set quadrats, yet a novel numerical-based approach using machine learning techniques was used to interpret data from fieldwork to assess the relationship between biotic and abiotic factors. A few scholars adopted a comparison or complementation approach where existing methods were compared to improve biodiversity assessment outcomes (Table 2). For example, Wicke and Fischer (2017) developed an enhanced software tool to prioritise species for biodiversity conservation programs by comparing and combining two biodiversity indices, the Shapley Value and Fair Proportion Index.

Field-based approaches were adopted more commonly (221) than laboratory-based approaches (160) (Table 2). Trapping, net and observation methods were the most common field approaches used (Fig S1). Trap methods were varied and included pitfall traps (Eyre and Leifert, 2012; Rosa et al., 2012), malaise traps (Oxbrough et al., 2010), hoop traps (Graham et al., 2010), Elliott box traps (Ward and Kutt, 2009), window traps (Obrist and Duelli, 2010) and others. Net methods were also varied and included sweep nets (Angélibert et al., 2010; Muelelwa et al., 2010), dip nets (Sewell et al., 2010), surber nets (Li et al., 2013) and others. Many scholars also adopted more traditional observation-based approaches (Ledo et al., 2012; Smith and Fisher, 2009; Tichit et al., 2010) and transect and quadrat methods (Geml et al., 2009; Gibson, 2011; Malik et al., 2019; O’hara et al., 2010). Infrequent approaches included snorkelling, scuba diving, Winkler bags and soil cores (Fig S1). Metabarcoding and other molecular techniques were the common laboratory-based approaches used to assess biodiversity (Bakker et al., 2019; Fernández et al., 2014; Prié et al., 2020; Serrana et al., 2019) such as DNeasy PowerSoil DNA Isolation Kit (Qiagen) to extract DNA (Bakker et al., 2019), use of mass-PCR amplification of the COI barcode gene (metabarcoding) from arthropods (Yu et al., 2012), use of eDNA metabarcoding from predator species to assess biodiversity and determine dietary composition (Nørgaard et al., 2021), and others (Fig S2).

Numerical approaches were widely used by scholars (198) in biodiversity assessments (Table 2). Namely, biodiversity indices (Eyre and Leifert, 2012; Urbieta et al., 2012; Yu et al., 2012) were most commonly adopted, followed by correlation (Graham et al., 2010; Mohapatra et al., 2007), Bray-Curtis similarity or dissimilarity (Ferrier et al., 2007; Muelelwa et al., 2010), principal component analysis (Kitching and Ashton, 2013; Williams et al., 2010), regression models (Khelifa, 2019; Obrist and Duelli, 2010) and others (Fig S3). Spatial approaches were adopted less frequently than numerical approaches, and most commonly included ArcGIS software. Namely, ArcGIS was used to map and extract buffer zones of rivers (Li et al., 2013), determine size of habitats and distance from roads and other infrastructure (Tichit et al., 2010), develop GIS spatial analytical tools to facilitate selection of site-specific biodiversity indicators (Burke et al., 2008), investigate relationship between landscape structure and biodiversity (Schindler et al., 2008) and others.

3.4 Geographic and environment-type trends

Most biodiversity assessments were implemented in large regions comprising combined environment types (Caesar et al., 2006; Gatti and Notarnicola, 2018; Moctezuma, 2021). For example, Moctezuma (2021) conducted fieldwork in the Mesoamerican region of Los Chimalapas in southern Mexico, comprising an area of 600,000 ha; wide elevational gradients, rivers, forests, valleys and many other environmental types are present in this region. Marine (Arvanitidis et al., 2005; Zhan et al., 2014), forest (Lindqvist et al., 2016; Schulze et al., 2020) and aquatic environments (Alam et al., 2020; Hammer et al., 2014) were also frequently used (Figure 3). Coastal environments were one of the least common environment-types (Arvanitidis et al., 2005; Castro et al., 2021; O’hara et al., 2010).

In North America, geodiversity terms were widely adopted, with hydrological (Figure 4(d)) being the most common, followed by soil (Figure 4(c)). Some articles used more than one study site, up to five study sites recognised geological components from three authors (Coops et al., 2009; Geml et al., 2009; Graham et al., 2010), and three geomorphological (Sherrington, 2005; Coops et al., 2009; Graham et al., 2010). There were no biodiversity assessments mentioning the influence of geodiversity on biodiversity (Figure 4(f)) in the sample of literature analysed, and two sites were used in biodiversity assessments including all components from the Gray (2013) definition of geodiversity (Coops et al., 2009; Graham et al., 2010). In South America, hydrological components (Figure 4(d)) were predominant, followed by soil (Figure 4(c)) and many geological components (Figure 4(a)). Geomorphological was least common with only two mentions (Callisto et al., 2005; Magris et al., 2019) (Figure 4(b)). There were no studies mentioning the influence of geodiversity on biodiversity (Figure 4(f)), and no studies encompassing all components of the geodiversity definition (Figure 4(e)).

In Europe, geodiversity component terms were widely adopted, with hydrological (Figure 4(d)) the most common, followed by soil (Figure 4(c)), geological (Figure 4(a)) and geomorphological (Figure 4(b)). There were no biodiversity assessments mentioning the influence of geodiversity on biodiversity (Figure 4(f)), and only one study site – Apulia region of Italy (Gatti and Notarnicola, 2018) – mentioned all components of geodiversity (Figure 4). In Asia, geodiversity terms were adopted less frequently, with only three mentions of geological terms (Jo et al., 2019; Liu et al., 2017; Witkowski et al., 2016) and two geomorphological (Roy and Behera, 2002; Matsumoto et al., 2017); hydrological (Figure 4(d)) were mentioned more frequently, followed by soil (Figure 4(c)). There were no complete definitions (Figure 4(e)), and the influence of geodiversity on biodiversity was not mentioned (Figure 4(f)). In South Africa, hydrological terms were most common (Figure 4(d)), followed by soil (Figure 4(c)). There were four mentions of geomorphological components (Asibor, 2015; Khelifa, 2019; Reyers et al., 2007; Roba and Oba, 2009), and three geological (Asibor, 2015; Burke et al., 2008; Roba and Oba, 2009), and only one mention of the influence of geodiversity on biodiversity (Figure 4(f)) (Wessels et al., 2000) and no complete definitions (Figure 4(e)). In Oceania, all study sites located in Australia mentioned a geodiversity-relevant term (Figure 4(a)–(d)), with hydrological being the most common (Figure 4(d)), followed by geological (Figure 4(a)). Further, compared to other countries, Australian study sites most frequently mentioned the influence of geodiversity on biodiversity (Figure 4(f)), and one of only few countries mentioning all components from the Gray (2013) geodiversity definition (Figure 4(e)).

Therefore, relative to the number of study sites used in biodiversity assessments across each continent (Figure 4), study sites in Oceania (Australia and New Caledonia) and North America (U.S.A and Canada) were arguably most inclusive of geodiversity, with Oceanian sites most frequently mentioning the influence of geodiversity on biodiversity (Figure 4(f)), and North American sites most commonly including all geodiversity component terms globally (Figure 4(e)). European scholars were the predominant contributors to biodiversity assessment from the literature analysed and based on the number of study site contributions (Figure 4), followed by Asia, South America, North America, Oceania and Africa.

4. Discussion

This study scientometrically analysed a representative sample of 240 biodiversity assessment publications with focus on species richness to investigate the degree of geodiversity inclusiveness, methodological trends, geographic trends in geodiversity inclusiveness, environment-type trends and suggestions for future directions in biodiversity assessment methods.

4.1 Geodiversity inclusiveness in biodiversity assessment and future directions

Soil and hydrological-related terms were adopted most frequently (Figure 2). Contrariwise, geological (Figure 4(a)) and geomorphological (Figure 4(b)) terms were adopted less frequently. The frequency of soil terms in the literature could be attributed to recent methodological advances (Table 2) in laboratory-based methods (Fig S2) such as high-throughput sequencing facilitating soil biodiversity assessment (Bahram et al., 2018; Geisen et al., 2018; Ramirez et al., 2018; Potapov et al., 2019; Thakur et al., 2020), or a recent shift in biodiversity research agendas highlighting the importance of soil in ecosystem functioning and services (Bardgett and Van Der Putten, 2014; Nielsen et al., 2015; Thakur et al., 2020), likely driven by the fact that soil is one of the most biodiverse terrestrial habitats on Earth (Thakur et al., 2020), and that soils provide many global ecosystem services, such as surface water-quality, building materials, culture, groundwater quality, recreation, and crop production and quality (Lehmann et al., 2020).

Hydrological (Figure 2) was the most frequently used geodiversity-related term and could be explained by the inherent role hydrology plays in species distributions, structure and maintenance (D’Odorico et al., 2010; Konar et al., 2013; Rodriguez-Iturbe and Rinaldo, 2001; Sankaran et al., 2005), or by the fact that climate change – a key contributor to biodiversity decline (Lindqvist et al., 2016) – influences hydrologic patterns that regulate ecological processes (Currie, 1991; Poff et al., 1997; Weltzin et al., 2003; Konar et al., 2013); hence, scholars recognise the importance of improving understanding of how hydrological elements influence biodiversity (Konar et al., 2013).

However, despite use of various geodiversity component terms (Figure 2), results suggest geodiversity inclusiveness as largely absent from the biodiversity assessment literature analysed, with ‘geology’ and ‘geomorphology adopted less frequently (Figure 2), infinitesimal mention of geodiversity influence on biodiversity (Figure 2), increasing trending value (Figure 1), and the absence of the term ‘geodiversity’. An interesting trend considering geodiversity assessment scholars more frequently mentioned the term ‘biodiversity’ in comparison (Crisp et al., 2021). The use discrepancy between ‘geodiversity’ and ‘biodiversity’, and prioritisation of biodiversity in both assessment and conservation literature (Chelariu and Hapciuc, 2017; Gray, 2018), could be explained simply by outright unfamiliarity of the term ‘geodiversity’ (Orsi, 2011), its inherent complexities as a multi-faceted and evolving geoscientific paradigm (Gray, 2021), or by the deep-rooted history biodiversity has in scientific literature having emerged earlier in the 1700s, compared to late 1970s for geodiversity (Ibáñez et al., 2019). Further, Boothroyd and McHenry (2019) indicated factors contributing to the exclusion of geodiversity in ecological and biological discourse, namely: the absence of easy-to-apply analogues between geodiversity and biodiversity, temporal dimensions inherent in geodiversity and the complex aetiologies of geodiversity can appear irrelevant to nature conservation approaches and assessment. For example, more simplistic ‘count’ values such as ‘species richness’ are intuitive and representative indicators for biodiversity value (Ahrendsen et al., 2016), while attributing a count value to geodiversity is subject to influence from temporal variables. Hence, aforementioned complexities (McHenry and Boothroyd, 2019), and the attributing developmental state in geodiversity assessment (Crisp et al., 2021) are probably linked to the trends above (Figure 2; Table 2).

Subsequent research needs to improve geodiversity inclusiveness in biodiversity assessment, and avenues for this could include development of novel methods combining geodiversity and biodiversity assessment with conservation endeavours such as nature conservation, research standardising terms and methods in geodiversity assessment (Crisp et al., 2021), and research increasing awareness of the importance of both geodiversity and biodiversity in holistic conservation endeavours such as ecosystem-based management or nature conservation. Crisp et al. (2021) suggested conferences, technical working groups, videoconferencing and workshops as viable avenues to increase geodiversity awareness, and with amalgamation of geodiversity and biodiversity in conservation endeavours.

4.2 Methodological trends and future directions

Field-based methods of trapping methods (Fig S1) were most commonly used by scholars in biodiversity assessments (Table 2), with use of quadrats, transects, net methods and observations also common. However, field-based methods can be logistically challenging at large scales (Müller and Brandl, 2009), require considerable resourcing (Kerr and Ostrovsky, 2003), time (Lopes et al., 2017), and may result in bias towards easily sampled species (Lecq et al., 2015). With the gargantuan and important task of assessing species richness globally (Webb et al., 2010; Costello et al., 2013), many scholars have developed novel, and other approaches (Table 2) to assess biodiversity such as laboratory-based (Fig S2) and numerical-based methods (Fig S2).

Laboratory-based methods using portable instruments have showed promise in expediting biodiversity sampling and analysis (Krehenwinkel et al., 2019). Numerical-based approaches such as statistical modelling methods allow expedition of biodiversity assessment with statistical extrapolation techniques used to estimate species richness to reduce sampling duration and effort required (Ward and Larivière, 2004). Spatial-based methods were used, though less commonly (Table 2), reducing dependence on field-based and laboratory-based requirements to expedite the biodiversity assessment process. For example, the monitoring of grasslands and surrounding biodiversity is traditionally achieved using field-based methods, but scholars have since demonstrated how remote sensing can improve grassland assessments across space and time (Kerr and Ostrovsky, 2003; Lopes et al., 2017; Pettorelli et al., 2014). Spatial-based methods such as remote sensing were adopted less commonly compared to field and laboratory-based methods perhaps because this methodological approach is currently limited mostly to vegetation-based biodiversity assessments (Wang and Gamon, 2019).

In the literature analysed, most scholars adopted traditional approaches to species richness assessment, with fewer novel or comparative approaches implemented (Table 2). Results suggest that the adoption of varying methodological approaches need funding, time and resourcing availabilities such as taxonomic expertise (Kerr and Ostrovsky, 2003; Miller, 2007; Carbayo and Marques, 2011; Yu et al., 2012; Yu et al., 2012, 2012; Costello et al., 2013; Gasc et al., 2015; Lopes et al., 2017). For example, (Ruengsawang et al., 2012) used existing field and laboratory-based techniques such as SCUBA diving and light microscopy to assess biodiversity of freshwater sponges in the Lower Mekong Basin, Thailand, while (Angélibert et al., 2010) developed a novel index method for rapid assessment of freshwater biodiversity in ponds.

4.3 Geographic and environment-type trends and future directions

The location of study sites (Figure 4) from each article could provide insight into economic, political, cultural or social (Schenk et al., 2007) barriers hindering geodiversity inclusiveness in biodiversity assessment, methodological trends and biodiversity conservation in subsequent research. For example, socioeconomic factors such as governance, corruption, conflict frequency and political border influence on policy and legislation are some factors hindering biodiversity assessment and conservation outcomes (Dallimer and Strange, 2015; Titley et al., 2021).

The preponderance of hydrological and soil-related terms was globally distributed across North America, Europe, South America, Asia, Africa and Oceania (Figure 4(c) and (d)). Hydrological-related terms were most common globally (Figure 4(d)); cultural or social factors explaining this trend are probably linked to the ecosystem services provided by hydrological elements such as fisheries or agriculture (Ramesh et al., 2015), and political and economic influence on this is probably attributed to the crucial role hydrology plays in global agriculture, mining, energy production, manufacturing and residential use (Brauman et al., 2016; D’Odorico et al., 2019; Mekonnen and Hoekstra, 2016; Vörösmarty et al., 2010). Further, given the various scales of politics such as international, national and state levels, more localised trends were apparent (Figure 4). For example, in South America, political influence over scientific funding could explain frequent appearance of soil-related terms, with Amazonian deforestation attributed to unprecedented agricultural expansion in South America (De Sy et al., 2015). Further, the frequency of hydrological-related terms could be explained by the highest global combined value of ecosystem services provided by the Amazon River and wetlands in South America (Wittmann and Junk, 2016). In Oceania, the term ‘geology’ appeared second-most commonly (Figure 4(a)) and could be explained by Australia’s relatively exploitative approach to geology (Brocx, 2007) or involvement of Australian scholars in the inception of the geodiversity concept (Sharples, 1993; Kiernan, 1996, 1997; Gray, 2004; Zwolinski, 2004; Serrano and Flano, 2007; Silva et al., 2015). In Europe, interest in the influence of geodiversity on biodiversity in the Apulia region of Italy (Figure 4(f)), could be explained by the fact that peculiar geological and geomorphological features – boulder accumulations from historical tsunami, rich fossiliferous associations, Cretaceous dolomitic and calcareous layers, and rocks showing distinct thin chert layers and slump features trigged by tectonic activity – have attracted numerous researchers over the last 150 years (Sansò et al., 2015), and the establishment of the ‘Biodiversity MARE Tricase’ project in 2015 which endeavours to inventory, research and promote coastal and marine biodiversity in the region (Micaroni et al., 2018). However, subsequent research is needed to expand upon and substantiate geodiversity inclusiveness trends with contributions from other continents (Figure 4). For example, subsequent research could use Oceanian countries such as Australia as a case study to understand why the term ‘geological’ was used second-most frequently and why Australia most frequently mentioned the influence of geodiversity on biodiversity (Figure 4(f)), or subsequent research could consider factors explaining the more frequent mention of all geodiversity component terms in North-American literature (Figure 4(e)). Further, subsequent research could use biodiversity assessments from the African continent as a case study (Figure 4) to determine factors hindering frequency of implementation and development of biodiversity assessment.

Subsequent research could also investigate factors explaining environment-types used in biodiversity assessments (Figure 3). For example, the high frequency of marine environments in biodiversity assessment could be influenced by economic factors such as transport, food production, mineral extraction, biotechnology, tourism and scientific research facilitated by marine environments (Kaczynski and others, 2011; Martínez-Vázquez et al., 2021). Conservation and political factors such as the United Nations Conference on Sustainable Development in 2012 deeming oceans as priority areas for conservation could also explain the frequency of marine environments in biodiversity assessment research. The frequency of marine environments could also be explained by the scientific and growing need to monitor ocean biodiversity, distribution, abundance and health in response to a rapidly changing climate since ocean ecosystems are essential the livelihoods of billions of people (Canonico et al., 2019). Further, the infrequent inclusion of coastal environments in biodiversity assessment is an interesting trend given the same was observed in geodiversity assessment literature (Crisp et al., 2021), and could be explained by the logistic complexities associated with fluctuating tides, rocky shores and cliff precipices to carry out coastal assessments, thus limiting effective monitoring and assessment of coastal biodiversity (Livore et al., 2021). Therefore, subsequent research could explore logistical, economic, scientific, conservation, cultural and other factors influencing inclusion of specific environment-types in biodiversity assessments, with exploration of the main text of articles as well as the abstract.

5. Conclusion

This study scientometrically analysed a representative sample of 240 biodiversity assessment publications with focus on species richness to investigate geodiversity inclusiveness, methodological trends, geographic trends, environment-type trends and future directions in biodiversity assessment methods. Results showed that combined geodiversity and biodiversity assessment approaches can benefit conservation outcomes through reduction of the bias toward biodiversity, improving collaboration between biologists and geoscientists, provision of efficacious indicators of conservation value, and facilitated dissemination and inclusion of geodiversity in conservation literature. Results showed that articles most frequently included geodiversity elements such as hydrological and soil, and few articles mentioned the influence of geodiversity on biodiversity, and ‘geodiversity’ was not mentioned in all articles analysed. Methodological trends showed that field-based biodiversity assessments such as trapping, net and observation techniques were most common, followed by followed transects, quadrats, net methods and observations. Geographic trends showed many potential economic, social, cultural and political factors influencing geodiversity inclusiveness in biodiversity assessments. For example, the term ‘soil’ appeared second most commonly across all continents except for Oceania where the term ‘geology’ appeared second-most commonly, probably explained by Australia’s relatively exploitative approach to geology or involvement of Australian scholars in the inception of the geodiversity concept. Subsequent research could substantiate geographic trends with biodiversity assessment contributions from other continents. Environment-type trends showed that most biodiversity assessments were implemented in regions comprising many environment-types, followed by marine, aquatic and forest-environments; coastal environments were scarcely included. Subsequent research could explore logistical, economic, scientific, conservation, cultural and other factors influencing inclusion of specific environment-types in biodiversity assessments. Inferences are limited by the narrow scope of literature analysed to biodiversity assessment methods focussed on species richness. For example, results showed that the volume of contributions from North American scholars are less than some other continents like Europe or South America, but this trend represents contributions only in the sample of literature analysed, with extensive contributions from North American scholars expected in other dimensions of biodiversity. Subsequent research could investigate the influence of factors such as funding, time and resourcing availabilities on methodological objectives and trends, and consider political, cultural, social or economic factors influencing geodiversity inclusiveness in biodiversity assessment.

Supplemental Material

Supplemental Material - Geodiversity inclusiveness in biodiversity assessment

Supplemental Material for Geodiversity inclusiveness in biodiversity assessment by Jake RA Crisp, Joanna C Ellison, Andrew Fischer, and Jia SD Tan in Progress in Physical Geography: Earth and Environment

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jake R A Crisp

Supplemental Material

Supplemental material for this article is available online.

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