A Bioregionalization of South Africa Based on Beetles (Coleoptera)
1
Discipline of Geography, University of KwaZulu-Natal, Westville Campus, PB X54001, Durban 4000, South Africa
2
School of Life Sciences, University of KwaZulu-Natal, Westville Campus, PB X54001, Durban 4000, South Africa
3
Department of Natural Resources, Sabaragamuwa University, P.O. Box. 2, Belihuloya 70140, Sri Lanka
*
Author to whom correspondence should be addressed.
Diversity 2024, 16(8), 454; https://doi.org/10.3390/d16080454
Submission received: 1 June 2024
/
Revised: 12 July 2024
/
Accepted: 30 July 2024
/
Published: 1 August 2024
(This article belongs to the Special Issue Diversity, Distribution and Zoogeography of Coleoptera)
Abstract
:Beetles represent the most diverse group of living organisms on Earth, yet there has been very little in the way of using beetle distributions in bioregionalization exercises. Here, we combine several small data sets for beetle distributions in South Africa to produce a list of morphospecies and to analyze their presence within twenty geographic units spanning the entire country. We find a diversity of fine scale assemblages in the east, but also a disjunction between the western arid/winter-rainfall and eastern summer-rainfall half of the country, which is in line with several previous studies based on diverse groups of animals and plants. We recommend the use of the increasing citizen science data sets in studying biogeographic patterns in groups such as beetles, which have so far received limited attention.
1. Introduction
The accuracy of bioregionalization schemata depends on the completeness of the records for the group of organisms under study. This is why tetrapod vertebrates and higher plants dominate the majority of the available schemata to date [1]. In contrast, invertebrates, which include the majority of both terrestrial and marine species, remain poorly explored in both regional [2] and global [3] studies, and in the latter case, these often have to rely on substantial and not inconsequential interpolation exercises.
The advent of online data from platforms such as the Global Biodiversity Information Facility and iNaturalist, is likely to be a gamechanger in terms of invertebrate data availability, albeit, at this stage, the distribution data are still vastly lagging behind those for larger-bodied organisms, and much effort is going into data cleaning methods [4]. Thus, at least regionally, museum collections may yet represent the most comprehensive data source. While these are still being digitized, one can refer to other distribution data, derived from diverse taxonomic and ecological studies. The question of course remains—how representative can such data be, even when combining the efforts of multiple researchers, for entire biogeographic provinces or regions? Can the results favorably compare with schemata based on well-collected taxa?
Given the educational and conservational relevance of bioregionalization at a time of rapid biodiversity loss [5], and when increased environmental awareness is called for [6], such questions need to be addressed urgently. It is particularly important that, at a time when small-bodied invertebrates are still underrepresented in online databases, and when museum collections are incompletely digitized, other data sources be found and their representativeness be assessed. Here, we use beetle (Coleoptera) records collated from a range of ecological and other studies conducted across sections of South Africa, and use them to delineate regional subdivisions and compare these with available schemata based on invertebrates as well as other taxa.
2. Methods
We collated data sets compiled with reference digital photographs for 5269 individual beetles recorded in South Africa (SA) between 2008 and 2016 (although, in some cases, these were published later), as part of several ecological, evolutionary, taxonomic, and biogeographic projects [7,8,9,10,11,12,13]; these were combined with collections assessed for an undergraduate course at the University of KwaZulu-Natal, and occasional collections by the authors of this study, gathered during the same time interval. The photographs were examined and classified into morphospecies based on external morphology, following guidelines based on numerous recent genus-level revisions across most of the major beetle families, but without considering any differences in genitalia. This resulted in morphospecies likely being able to equate to actual species in most cases, but in some cases, potentially incorporating two or more species from the same genus. Following this method, the individual beetles were allocated to 2407 morphospecies representing 83 families based on the most recent classification for all beetles [14,15]. Collection localities were digitized and the coordinates assigned to operational geographic units (OGUs) were modified from a recent reference used in a similar study [16], as per Table 1 and illustrated in Figure 1. This resulted in a presence–absence matrix for morphospecies according to geographic units (Supplementary Table S1), which was then phenetically analyzed (Unweighted Pair Group Method with Arithmetic Mean (UPGMA) with the Jaccard similarity coefficient, as used in key bioregionalization studies [17]) using the PAST 3.22 software package [18], to determine the similarity between mapping units based on their assemblages of beetle morphospecies. Singletons (i.e., morphospecies present on only a single geographic unit) were removed prior to analyses [19].
3. Results
Where specimen and morphospecies representation across South Africa is concerned, the best represented were the OGUs where the included projects were centered, with most specimens being collected in coastal areas (Table 1). This was reflected in the number of morphospecies too, including in areas close to the cities of Durban (eThekwini Municipality), with exactly 800 morphospecies, followed by Cape Town, George, and Gqeberha (with 384, 295, and 242 morphospecies, respectively); the poorest representation was for the OGUs most distant from these (largely coinciding with the arid parts of South Africa—Nama karoo and Succulent karoo), with only 18 morphospecies for these two units combined.
The clusters of OGUs detected by our analyses were each spatially contiguous at 0.03 and 0.06 similarity on the resulting phenogram (Figure 2), and these sets of clusters were, respectively, mapped as main clusters and sub-clusters. At 0.03 similarity, three clusters were distinguished. Cluster 1 is composed of three units—NMSPB (Northern Mopane and Soutpansberg), WLB (Wolkberg), and NMV (Northern Middelveld). It is located in the northern summer-rainfall parts of SA and has tropical affinities, incorporating most of the Limpopo province, a large part of the Mpumalanga province, as well as northern parts of the Northwest province and Eswatini. This is the smallest cluster in terms of area, and is predominately a savanna cluster and, to a lesser degree, includes parts of the grassland biome. At 0.06 similarity, Cluster 1 is composed of two sub-clusters (1.1 and 1.2). Sub-cluster 1.1 is composed of NMSPB and WLB mostly in Limpopo and savanna biome, while sub-cluster 1.2 is composed only of NMV, which covers Eswatini-Eastern Mpumalanga and is also dominated by savanna.
Cluster 2 is composed of ten OGUs of SA (NCB (Natal Coastal Belt), PND (Pondoland), NGO (Ngoye), SMP (Southern Maputaland), SMV (Southern Middelveld), KMD (KwaZulu-Natal Midlands), EE (Eastern Escarpment), NME (Northern Mpumalanga Escarpment), STR (Southern Transkei Coastal Belt), and CBV (Central Bushveld)). This is a more temperate–subtropical cluster, including the South African provinces of KwaZulu-Natal, Gauteng, North West, and partly Mpumalanga and Eastern Cape (composed predominately of savanna and grassland biomes, with forest pockets). Cluster 2 has four sub-clusters at 0.06 similarity. Sub-cluster 2.1 has five units (NCB, PND, NGO, SMP, and SMV), including coastal KwaZulu-Natal and adjacent inland areas, as well as most of Eswatini (biome mosaics with savanna, grassland, and forest, as well as within the Indian Ocean Coastal Belt, best exemplified by the STR, PND, and NCB units in Table 1). Sub-cluster 2.2 has three units (KMD, EE, and NME) and covers inland KwaZulu-Natal, Gauteng, and western Mpumalanga. It is largely congruent with the grassland biome. Both sub-clusters 2.3. and 2.4. have a single unit each. Sub-cluster 2.3. is coastal and composed of STR (coastal Eastern Cape) with biome mosaics of savanna, grassland, and forest, as well as within the Indian Ocean Coastal Belt. Sub-cluster 2.4. is further inland and equates to the CBV unit, which is centered in the North West Province, straddling savanna and grassland vegetation.
Cluster 3 has seven units viz. CFR (Cape Floristic Region), KNY (Knysna), ACB (Albany Coastal Belt), SKR (Succulent Karoo), ECE (Eastern Cape Escarpment), NKR (Nama Karoo), and HVD (Highveld). This cluster covers the largest area including the arid parts of South Africa, as well as the winter-rainfall Cape Floristic Region (the Western Cape, Northern Cape, and parts of the Eastern Cape and Free State provinces of SA, as well as Lesotho). It includes the entire Fynbos, Succulent Karoo, Nama Karoo, and desert biomes; large parts of the Albany Thicket; and more arid sections of the grassland and savanna biomes. There are two sub-clusters at similarity 0.06, with Cluster 3 dividing into an arid and a winter-rainfall cluster. Sub-cluster 3.1 covers the Eastern, Western, and Northern Cape provinces and is composed of CFR, KNY, and ACB (winter-rainfall area) OGUs. Sub-cluster 3.2 is composed of the four remaining OGUs (SKR, ECE, NKR, and HVD). This is a large, mostly arid area covering the Northern Cape, Eastern Cape, Free State, Lesotho, and parts of the North West and Limpopo provinces.
4. Discussion
4.1. Spread of Collection Points
The collection localities were not evenly spread across SA. The vast majority of the collection points were close to the institutions that hosted the research projects (mostly the University of KwaZulu-Natal in Durban and Pietermaritzburg (KwaZulu-Natal), but also Nelson Mandela University in Gqeberha (formerly Port Elizabeth; Eastern Cape) and the University of Cape Town in Cape Town (Western Cape)), which biased the representation across geographic units (Table 1) (see [20] for a discussion on geographical bias in data for land snails in southern Africa). Nevertheless, at least a few records were available from each geographic unit, even after eliminating the morphospecies from single geographic units (i.e., singletons).
4.2. Regional Patterns
It is common for the main biogeographic disjunction in southern Africa to be either between the arid west and the mesic east, or between the winter-rainfall southwest and the rest of the region [21]. This study also illustrates a distinct western cluster, and a further disjunction in the west between the winter-rainfall southwest and the arid remainder. However, our primary disjunction is between the more tropical lowveld savanna (Limpopo and eastern Mpumalanga; Cluster 1 in Figure 3) and the rest, as has previously been retrieved in freshwater fishes, amphibians, and mammals [21], in all vertebrates [16], as well as in land snails [20]. These disjunctions largely match those shown here at 0.03 or 3% similarity (Figure 3). The finer subdivisions observed in the east and north at 0.06 (6%) similarity are possibly artifacts resulting from limited sampling, although a distinctive cluster centered around the North West province is arguably equivalent to the one retrieved for birds (see [21]).
It is remarkable and reassuring that even with limited and biased sampling as reported here, it is possible to reconstruct broad regional biotic assemblage patterns similar to those acknowledged based on much larger data sets in organisms that are much better surveyed. It is, at the same time, important to understand how far this reliability goes in terms of finer subdivisions (cf. [16]).
The approach taken here, based on ad hoc collections, is likely to contain mainly common species, and very few rare ones. In a sense, this is similar to vegetation classification, which often places an emphasis on dominant taxa, and the opposite of the endemics-focused previous approach to vertebrate biogeography in Southeast Africa [16]. Insect morphospecies are in fact good biome indicators [22]. However, this does not seem to play an important role here, as the biogeographic units retrieved correspond better to those for endemic vertebrates than to biomes. This may have to do with multiple cryptic beetle species being lumped as one morphospecies in certain instances, but the broader species group is quite correctly defined and is associated with one or a few biome(s) or vegetation type(s). It is with great excitement that we are expecting regional pattern studies using citizen science-based data sets as they increase in size, and as their biases become better understood [23]. In the meantime, our study indicates that combining other data sources as available may provide reasonably reliable bioregionalization schemata.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d16080454/s1, Table S1: Presence/absence matrix for beetle morphospecies by biogeographic unit (abbreviations as in Table 1 in the article).
Author Contributions
Conceptualization, Ş.P.; methodology, S.R. and S.J.P.; formal analysis, A.K.S.; original draft preparation, A.K.S. and Ş.P.; writing—review and editing, all authors participated in equal measure. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Data Availability Statement
The distribution data are available in the Supplementary Materials.
Acknowledgments
Ş.P. and S.R. thank UKZN for research support.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Map of South Africa (red outline) with the geographic units used in the analysis (modified from [16] based on vertebrates); see Table 1 for further details.
Figure 2.
Bioregionalization of South Africa based on beetle occurrences: phenogram of the operational geographic units, with phenon cutoffs represented by red lines at 0.03 (clusters) and 0.06 (sub-clusters) (abbreviations for the regions are given in Table 1). Clusters and sub-clusters in shades of blue as mapped in Figure 3.
Figure 2.
Bioregionalization of South Africa based on beetle occurrences: phenogram of the operational geographic units, with phenon cutoffs represented by red lines at 0.03 (clusters) and 0.06 (sub-clusters) (abbreviations for the regions are given in Table 1). Clusters and sub-clusters in shades of blue as mapped in Figure 3.
Figure 3.
Bioregionalization of South Africa based on beetle occurrences: maps show the geographic location of the three clusters differentiated at 0.03 similarity (a) and eight sub-clusters differentiated at 0.06 (b).
Figure 3.
Bioregionalization of South Africa based on beetle occurrences: maps show the geographic location of the three clusters differentiated at 0.03 similarity (a) and eight sub-clusters differentiated at 0.06 (b).
Table 1.
The operational geographic units of analysis (modified from [16]) and the corresponding biomes and bioregions.
Table 1.
The operational geographic units of analysis (modified from [16]) and the corresponding biomes and bioregions.
| Biogeographical Unit Code (and Full Name) | Biomes (and Bioregions) Included | Number of Beetle Morphospecies |
|---|---|---|
| ACB (Albany Coastal Belt) | Savanna (Sub-Escarpment); Grassland (Sub-Escarpment, Drakensberg); Zonal and Intrazonal Forest; Nama Karoo (Lower); Fynbos (Eastern Fynbos-Renosterveld) | 86 |
| ECE (Eastern Cape Escarpment) | Grassland (Dry Highveld, Sub-Escarpment, Mesic Highveld, Drakensberg); Savanna (Sub-Escarpment); Nama Karoo (Upper); Forest | 32 |
| CBV (Central Bushveld) | Savanna (Central Bushveld); Grassland (Mesic Highveld, Dry Highveld) | 31 |
| CFR (Cape Floristic Region) | Fynbos (Southwest, Northwest, West Coast and Karoo Renosterveld, Eastern Fynbos-Renosterveld); Succulent Karoo (Namaqualand Hardeveld, Trans-Escarpment, Knersvlakte, Rainshadow Valley); Forest | 79 |
| KNY (Knysna) | Fynbos (Eastern and Western Fynbos-Renosterveld, South Coast and Southern Fynbos, South and East Coast Renosterveld); Forest; Succulent Karoo (Rainshadow Valley) | 114 |
| NCB (Natal Coastal Belt) | Grassland (Sub-Escarpment); Savanna (Sub-Escarpment); Forest | 259 |
| HVD (Highveld) | Grassland (Dry and Mesic Highveld); Savanna (Eastern Kalahari Bushveld) | 25 |
| NGO (Ngoye) | Forest; Grassland (Sub-Escarpment, Mesic Highveld); Savanna (Lowveld) | 70 |
| KMD (KwaZulu-Natal Midlands) | Forest; Savanna (Sub-Escarpment); Grassland (Sub-Escarpment, Drakensberg) | 68 |
| NME (Northern Mpumalanga Escarpment) | Forest; Savanna (Central Bushveld, Lowveld); Grassland (Mesic Highveld) | 35 |
| NMSPB (Northern Mopane and Soutpansberg) | Savanna (Central Bushveld, Lowveld, Mopane); Grassland (Mesic Highveld); Forest | 30 |
| NMV (Northern Middelveld) | Savanna (Lowveld, Mopane, Central Bushveld); Grassland (Mesic Highveld); Forest | 17 |
| PND (Pondoland) | Forest; Grassland (Sub-Escarpment); Savanna (Sub-Escarpment) | 90 |
| SKR (Succulent Karoo) | Nama Karoo (Lower, Upper, Bushmanland); Succulent Karoo (Trans-Escarpment, Rainshadow Valley, Namaqualand Cape Shrublands, Hardeveld and Sandveld, Knersvlakte); Fynbos (Eastern and Western Fynbos-Renosterveld, Northwest Fynbos, Karoo Renosterveld); Desert (Southern Namib) | 7 |
| EE (Eastern Escarpment) | Grassland (Mesic Highveld, Sub-Escarpment, Drakensberg); Forest | 32 |
| SMP (Southern Maputaland) | Forest; Savanna (Lowveld) | 68 |
| SMV (Southern Middelveld) | Grassland (Sub-Escarpment, Mesic Highveld); Savanna (Lowveld); Forest | 49 |
| STR (Southern Transkei Coastal Belt) | Forest; Savanna (Sub-Escarpment); Grassland (Sub-Escarpment) | 29 |
| NKR (Nama Karoo) | Savanna (Eastern Kalahari Bushveld, Kalahari Duneveld); Nama Karoo (Upper, Lower, Bushmanland); Succulent Karoo (Richtersveld); Grassland (Dry Highveld, Sub-Escarpment, Drakensberg); Desert (Gariep) | 21 |
| WLB (Wolkberg) | Forest; Savanna (Lowveld); Grassland (Mesic Highveld) | 25 |
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MDPI and ACS Style
Summersgill, A.K.; Procheş, Ş.; Ramdhani, S.; Perera, S.J. A Bioregionalization of South Africa Based on Beetles (Coleoptera). Diversity 2024, 16, 454. https://doi.org/10.3390/d16080454
AMA Style
Summersgill AK, Procheş Ş, Ramdhani S, Perera SJ. A Bioregionalization of South Africa Based on Beetles (Coleoptera). Diversity. 2024; 16(8):454. https://doi.org/10.3390/d16080454
Chicago/Turabian StyleSummersgill, Amy K., Şerban Procheş, Syd Ramdhani, and Sandun J. Perera. 2024. "A Bioregionalization of South Africa Based on Beetles (Coleoptera)" Diversity 16, no. 8: 454. https://doi.org/10.3390/d16080454
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