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Climate drives community‐wide divergence within species over a limited spatial scale: evidence from an oceanic island

DOI: 10.1111/ele.13433
Authors:
Antonia Salces-Castellano at University of Liege and Free University of Brussels
Antonia Salces-Castellano
  • University of Liege and Free University of Brussels
Jairo Patiño at Spanish National Research Council
Jairo Patiño
Nadir Alvarez at Natural History Museum of Geneva
Nadir Alvarez
Carmelo Andújar at Imperial College London
Carmelo Andújar
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Abstract and Figures

Geographic isolation substantially contributes to species endemism on oceanic islands when speciation involves the colonisation of a new island. However, less is understood about the drivers of speciation within islands. What is lacking is a general understanding of the geographic scale of gene flow limitation within islands, and thus the spatial scale and drivers of geographical speciation within insular contexts. Using a community of beetle species, we show that when dispersal ability and climate tolerance are restricted, microclimatic variation over distances of only a few kilometres can maintain strong geographic isolation extending back several millions of years. Further to this, we demonstrate congruent diversification with gene flow across species, mediated by Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact. The unprecedented scale of parallel species responses to a common environmental driver for evolutionary change has profound consequences for understanding past and future species responses to climate variation. Using a community of beetle species, we show that when dispersal ability and climate tolerance are restricted, microclimatic variation over distances of only a few kilometres can maintain strong geographic isolation extending back several millions of years. Further to this, we demonstrate congruent diversification with gene flow across species, mediated by Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact.
Sampling plots within the laurel forest of the Anaga peninsula of Tenerife, Canary Islands. The ten sampling plots are labelled, from east to west, S1–S10, and the potential extent of the laurel forest within the Anaga peninsula (del Arco Aguilar et al. 2012) is shown in green. Top right inset shows typical laurel forest vegetation.
Sampling plots within the laurel forest of the Anaga peninsula of Tenerife, Canary Islands. The ten sampling plots are labelled, from east to west, S1–S10, and the potential extent of the laurel forest within the Anaga peninsula (del Arco Aguilar et al. 2012) is shown in green. Top right inset shows typical laurel forest vegetation.
… 
Patterns of community similarity among sampling plots. (a) Dissimilarity matrices among the ten sampling plots are presented at the level of presumed biological species (PBS) and four different treatments of intraspecific genetic variation: phylogroups, average NST and GST across all PBS, and all haplotypes across all PBS, where red represents maximum similarity between plots and light yellow represents maximum dissimilarity. Numbers in brackets correspond to the number of PBS sampled for each analysis, with the exception of haplotypes, where it represents the total number of haplotypes analysed. Sampling sites S1–S10 correspond to Fig. 1. (b) Hierarchical cluster analyses of dissimilarity matrices in A, with inset showing the increase in the average within‐group pairwise similarity (y axis = homogeneity) across the different number of groups of plots defined by the hierarchical classification (x axis). Repeated clustering of the four eastern and six western plots for the four treatments of intraspecific genetic variation, together with maximum increase in homogeneity, is highlighted with yellow and blue colours, respectively. Significant differences between the four eastern vs the six western plots defined for each of the four dissimilarity matrices of intraspecific genetic variation were revealed by Permanova analyses. (c) Landscape resistance analyses showing optimal models (indicated with shading, separated by a dashed line when there is more than one optimal model) that explain genetic structure within each of the dissimilarity matrices in A. ELEV = elevation, TEMP = temperature, PREC = precipitation, RUG = topographic rugosity, POS = topographic position index, WET = topographic wetness index, DIST = Euclidean distance, NULL = null model. See Table S3 for specific details.
Patterns of community similarity among sampling plots. (a) Dissimilarity matrices among the ten sampling plots are presented at the level of presumed biological species (PBS) and four different treatments of intraspecific genetic variation: phylogroups, average NST and GST across all PBS, and all haplotypes across all PBS, where red represents maximum similarity between plots and light yellow represents maximum dissimilarity. Numbers in brackets correspond to the number of PBS sampled for each analysis, with the exception of haplotypes, where it represents the total number of haplotypes analysed. Sampling sites S1–S10 correspond to Fig. 1. (b) Hierarchical cluster analyses of dissimilarity matrices in A, with inset showing the increase in the average within‐group pairwise similarity (y axis = homogeneity) across the different number of groups of plots defined by the hierarchical classification (x axis). Repeated clustering of the four eastern and six western plots for the four treatments of intraspecific genetic variation, together with maximum increase in homogeneity, is highlighted with yellow and blue colours, respectively. Significant differences between the four eastern vs the six western plots defined for each of the four dissimilarity matrices of intraspecific genetic variation were revealed by Permanova analyses. (c) Landscape resistance analyses showing optimal models (indicated with shading, separated by a dashed line when there is more than one optimal model) that explain genetic structure within each of the dissimilarity matrices in A. ELEV = elevation, TEMP = temperature, PREC = precipitation, RUG = topographic rugosity, POS = topographic position index, WET = topographic wetness index, DIST = Euclidean distance, NULL = null model. See Table S3 for specific details.
… 
Regional structuring of beetle genetic variation across the Anaga peninsula of Tenerife. Based on East‐West regional patterns of mtDNA structure, ddRAD‐seq data was generated for eight PBS (Table S1). (a) Haplotype networks constructed with ddRAD‐seq data using the NeighborNet method in SplitsTree. The four eastern and six western plots are highlighted with yellow and blue colours, respectively, and codes correspond to sampling locations presented in Fig. 1. Each circle represents an individual, scale bars represent the proportion of inferred mutational change along branches. (b) Ancestry estimation matrices from model‐based clustering analyses, using ddRAD‐seq data. When optimal K (number of inferred ancestral populations) was inferred to be two or three, this is indicated with K = 2 or K = 3 respectively. K was forced to two for Cardiophorus sp. and Leipaspis lauricola, where optimal K was inferred to be one, to test the specific hypothesis of regional structuring between eastern and western populations. Blue and yellow represent proportion of genomic assignment to eastern and western ancestry, respectively. In the case of R. persimilis, a third (central) ancestral population is represented in pink. See Fig. S1 for mtDNA haplotype networks and ddRAD‐seq PCA plots for each PBS, and all results for eight PBS with no regional structuring of mtDNA.
Regional structuring of beetle genetic variation across the Anaga peninsula of Tenerife. Based on East‐West regional patterns of mtDNA structure, ddRAD‐seq data was generated for eight PBS (Table S1). (a) Haplotype networks constructed with ddRAD‐seq data using the NeighborNet method in SplitsTree. The four eastern and six western plots are highlighted with yellow and blue colours, respectively, and codes correspond to sampling locations presented in Fig. 1. Each circle represents an individual, scale bars represent the proportion of inferred mutational change along branches. (b) Ancestry estimation matrices from model‐based clustering analyses, using ddRAD‐seq data. When optimal K (number of inferred ancestral populations) was inferred to be two or three, this is indicated with K = 2 or K = 3 respectively. K was forced to two for Cardiophorus sp. and Leipaspis lauricola, where optimal K was inferred to be one, to test the specific hypothesis of regional structuring between eastern and western populations. Blue and yellow represent proportion of genomic assignment to eastern and western ancestry, respectively. In the case of R. persimilis, a third (central) ancestral population is represented in pink. See Fig. S1 for mtDNA haplotype networks and ddRAD‐seq PCA plots for each PBS, and all results for eight PBS with no regional structuring of mtDNA.
… 
Repeated regional structuring of beetle intraspecific nuclear genomic variation across the Anaga peninsula. The Anaga peninsula is shown looking down onto its northern flank, with sampling sites labelled as in Fig. 1. Broken black lines denote the limits of a mega‐landslide estimated to have occurred between 4.1 and 4.7 Ma (Walter et al. 2005). Different ancestral populations inferred from individual ancestry coefficients estimated with the program sNMF (see Methods) are represented with different colours. Twelve of sixteen species of beetle analysed with ddRAD‐seq data (see Fig. S1) present evidence for regional structuring of inferred ancestral gene pools with geographically intermediate admixture, each fitting one of three models: (a) eastern (yellow) and western (blue) populations and admixed genomes centred around S5 and S6. This model is observed for the ten species pictured, from left to right, Paradromius amoenus, Acalles globulipennis, Laparocerus subnodosus, Laparocerus impressicollis, Leipaspis lauricola, Amaroschema gaudini, Silvacalles instabilis, Xestus throscoides, Laparocerus ellipticus and Chrysolina costalis; (b) Cardiophorus sp. (pictured) is separated into eastern (yellow) and western (blue) populations, distributed either side of admixed genomes in S4; (c) Rhopalomesites persimilis (pictured) is separated into eastern (yellow), central (pink) and western (blue) populations, with admixed genomes in geographically intermediate plots between east and central (S4) and central and west (S6).
Repeated regional structuring of beetle intraspecific nuclear genomic variation across the Anaga peninsula. The Anaga peninsula is shown looking down onto its northern flank, with sampling sites labelled as in Fig. 1. Broken black lines denote the limits of a mega‐landslide estimated to have occurred between 4.1 and 4.7 Ma (Walter et al. 2005). Different ancestral populations inferred from individual ancestry coefficients estimated with the program sNMF (see Methods) are represented with different colours. Twelve of sixteen species of beetle analysed with ddRAD‐seq data (see Fig. S1) present evidence for regional structuring of inferred ancestral gene pools with geographically intermediate admixture, each fitting one of three models: (a) eastern (yellow) and western (blue) populations and admixed genomes centred around S5 and S6. This model is observed for the ten species pictured, from left to right, Paradromius amoenus, Acalles globulipennis, Laparocerus subnodosus, Laparocerus impressicollis, Leipaspis lauricola, Amaroschema gaudini, Silvacalles instabilis, Xestus throscoides, Laparocerus ellipticus and Chrysolina costalis; (b) Cardiophorus sp. (pictured) is separated into eastern (yellow) and western (blue) populations, distributed either side of admixed genomes in S4; (c) Rhopalomesites persimilis (pictured) is separated into eastern (yellow), central (pink) and western (blue) populations, with admixed genomes in geographically intermediate plots between east and central (S4) and central and west (S6).
… 
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LETTER Climate drives community-wide divergence within species over
a limited spatial scale: evidence from an oceanic island
Antonia Salces-Castellano,
1,2†
Jairo Pati~
no,
1,3†
Nadir Alvarez,
4
Carmelo And
ujar,
1
Paula Arribas,
1
Juan Jos
e Braojos-Ruiz,
5
Marcelino del Arco-Aguilar,
3
V
ıctor Garc
ıa-Olivares,
1,2
Dirk N. Karger,
6
Heriberto L
opez,
1
Ioanna Manolopoulou,
7
Pedro Orom
ı,
8
Antonio
J. P
erez-Delgado,
1,2
William E. Peterman,
9
Kenneth F. Rijsdijk
10
and
Brent C. Emerson
1
*
Abstract
Geographic isolation substantially contributes to species endemism on oceanic islands when speci-
ation involves the colonisation of a new island. However, less is understood about the drivers of
speciation within islands. What is lacking is a general understanding of the geographic scale of
gene flow limitation within islands, and thus the spatial scale and drivers of geographical specia-
tion within insular contexts. Using a community of beetle species, we show that when dispersal
ability and climate tolerance are restricted, microclimatic variation over distances of only a few
kilometres can maintain strong geographic isolation extending back several millions of years. Fur-
ther to this, we demonstrate congruent diversification with gene flow across species, mediated by
Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact.
The unprecedented scale of parallel species responses to a common environmental driver for evo-
lutionary change has profound consequences for understanding past and future species responses
to climate variation.
Keywords
Arthropod, beetle, climate, dispersal, gene flow, Quaternary, speciation, topography.
Ecology Letters (2020) 23: 305–315
INTRODUCTION
Islands are often viewed as theatres for adaptive evolutionary
change and speciation (Losos & Ricklefs 2009), where non-
adaptive paths to speciation are frequently given limited
importance, or even ignored (e.g. Cabral et al. 2019). Recent
models have sought to understand how geophysical island
attributes influence insular biodiversity through the regulation
of colonisation, speciation and extinction (e.g. Whittaker
et al. 2008; Fern
andez-Palacios et al. 2016; Lim & Marshall
2017). Although such models have sought to account for both
long-term island ontogeny (e.g. Whittaker et al. 2008; Borre-
gaard et al. 2017) and shorter-term surface area changes (e.g.
Fern
andez-Palacios et al. 2016; Weigelt et al. 2016), insular
species themselves have received much less attention (but see
Rosindell & Harmon 2013; Rominger et al. 2016; Cabral
et al. 2019 for models incorporating species properties and
biotic interactions). While much quantitative molecular data
exists on insular speciation across archipelagos (e.g. Shaw &
Gillespie 2016), it is difficult to compare across such studies
and generalise about the drivers of speciation within islands.
This is because independent studies are idiosyncratic with
regard to geographic sampling, and tend to be taxonomically
biased toward diversified lineages. More importantly, such
studies are largely focussed on describing patterns of specia-
tion rather than the underlying processes that give rise to spe-
ciation. Despite these limitations, support has been found for
a model where there is a significant correlation between the
spatial scales of gene flow and speciation (Kisel & Barra-
clough 2010). However, the importance and mechanistic basis
of non-adaptive evolutionary pathways to speciation, where
physical disruption of gene flow initiates divergence and ulti-
mately promotes speciation, remains poorly understood.
Goodman et al. (2012) have noted that relatively rapid geo-
logical events within islands may be important drivers of
divergence and speciation, and several studies implicate the
fragmenting actions of volcanic activity (e.g. Vandergast et al.
2004) and flank collapses (e.g. Brown et al. 2006) as dispersal
1
Island Ecology and Evolution Research Group, Institute of Natural Products
and Agrobiology (IPNA-CSIC), C/Astrof
ısico Francisco S
anchez 3, La Laguna,
Tenerife, Canary Islands 38206, Spain
2
School of Doctoral and Postgraduate Studies, University of La Laguna,
38200, La Laguna, Tenerife, Canary Islands, Spain
3
Plant Conservation and Biogeography Group, Department of Botany, Ecol-
ogy and Plant Physiology, University of La Laguna, C/ Astrof
ısico Francisco
S
anchez, 38206,La Laguna, Tenerife, Canary Islands, Spain
4
Natural History Museum of Geneva, 1 route de Malagnou, 1208, Geneva,
Switzerland
5
Tenerife Insular Water Council (CIATF), C/ Leoncio Rodr
ıguez 2, 38003, Santa
Cruz de Tenerife, Spain
6
Swiss Federal Research Institute WSL, Z€
urcherstrasse 111,8903,Birmensdorf,
Switzerland
7
Department of Statistical Science, University College London, London, UK
8
Department of Animal Biology, Edaphology and Geology, University of
Laguna, C/ Astrof
ısico Francisco S
anchez, 38206, La Laguna, Tenerife, Canary
Islands, Spain
9
School of Environmental and Natural Resources, The Ohio State University,
Columbus, OH, USA
10
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam,
Amsterdam, Netherlands
*Correspondence: E-mail: bemerson@ipna.csic.es
†
These authors contributed equally to this work.
©2019 John Wiley & Sons Ltd/CNRS
Ecology Letters, (2020) 23: 305–315 doi: 10.1111/ele.13433
... Ecological speciation involving climatic-niche shifts has been described as an essential process generating diversity within oceanic island biotas (Gillespie et al., 2001). However, recent studies focused on arthropod assemblages have highlighted an important role for climatic niche conservatism as a driver of community assembly and diversification within islands (Lim et al., 2021;Salces-Castellano et al., 2020). ...
... Dispersal is a key process shaping island biotas, being fundamental for colonization and consequential within islands for the geographic structuring of genetic variation within species, speciation, and intra-island diversification (Gillespie et al., 2012;Salces-Castellano et al., 2020;Warren et al., 2015). Integrating across the distances and frequencies over which active and passive dispersal processes contribute to species cohesion and speciation ( Figure 1) provides a predictive framework for evolutionary trajectories at the level of F I G U R E 5 (a) Venn diagrams showing distribution of OTUs and (b) 15% lineages among habitats (laurel forest, La; pine forest, Pi; dry scrubland, Ds; and thermophilous woodland, Tw). ...
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Full-text available
  • May 2022
Specialisation to the soil environment is expected to constrain the spatial scale of diversification within animal lineages. In this context, flightless arthropod lineages, adapted to soil environments, but with broad geographical ranges, represent something of an anomaly. Here we investigate the diversification process within one such ‘anomalous’ soil specialist, an eyeless and flightless beetle species strongly adapted to the endogean environment but distributed across several oceanic islands. Canary Islands. Geomitopsis franzi Coiffait, 1978 (Coleoptera, Staphylinidae). We performed an integrative study, including molecular phylogenetics, population genomics and morphometry. Four DNA regions (two mitochondrial and two nuclear) were amplified and sequenced for 159 specimens from 58 localities sampled across five islands for phylogenetic analyses, and a dated phylogenetic tree was obtained using a mitogenome dataset. ddRAD‐seq data were generated to evaluate mtDNA lineages in sympatry against the biological species concept. We found high levels of genetic differentiation (>8% COI gene divergence) among populations from different islands and among geographically coherent lineages within single islands. Lineages within Tenerife presented significant patterns of isolation by distance, with ddRAD‐seq providing evidence that lineages represent biological species. Morphometric analyses revealed limited variation. Geomitopsis franzi is comprised of at least seven lineages that merit consideration as biological species, and is best considered as a complex of cryptic species. The limited morphological variation across these lineages is consistent with adaptation to the endogean environment placing strong constraints on morphological change. The evolution of cryptic species should be favoured when such constraints are coupled with limited dispersal ability, a trait that broadly characterises the soil mesofauna.
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Collective and harmonised high throughput barcoding of insular arthropod biodiversity: toward a Genomic Observatories Network for islands
Preprint
  • Mar 2022
Our current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species. This is due to inherent problems with obtaining high-quality arthropod data. Novel high throughput sequencing approaches are now emerging as powerful tools to overcome such limitations, and thus comprehensively address existing shortfalls in arthropod biodiversity data. Here we explore how, as a community, we might most effectively exploit these tools for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first review the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We consider how this can be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explore how these approaches can be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identify seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonised efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.
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Interactions between ecological, evolutionary and environmental processes unveil complex dynamics of insular plant diversity
Article
Full-text available
Aims: Understanding how biodiversity emerges and how it varies in space and time requires integration of the underlying processes that affect biodiversity at different levels of ecological organization. We present BioGEEM (BioGeographical Eco-Evolutionary Model), a spatially explicit model that integrates theories and processes understood to drive biodiversity dynamics. We investigated the necessary degree of mechanistic complexity by exploring simulation experiments to evaluate the relative roles of the underlying processes across spatio-temporal scales and ecological levels (e.g. populations, species, communities). Location: Hypothetical oceanic islands. Methods: BioGEEM is stochastic and grid-based, and it integrates ecological (meta-bolic constraints, demography, dispersal and competition), evolutionary (mutation and speciation) and environmental (geo-climatic dynamics) processes. Plants on oce-anic islands served as a model system. We ran the simulations both with all processes on and with selected processes switched off to assess the role of each process from the emergent patterns. Results: The full model was able to generate patterns matching empirical evidence and theoretical expectations. Population sizes were largest on young islands, and species, particularly endemics, better filled their potential range on young and old islands due to limited area and reduced competition. Richness peaked at mid-elevations. The proportion of endemics was highest in old, large and isolated environments within the islands. Species and trait richness showed unimodal temporal trends. Switching off selected processes led to several unrealistic patterns, including the evolution of super-dominant species, extremely high richness and weakened spatial diversity gradients. Main conclusions: The main predictions derived from BioGEEM are: Competition has cross-scale effects on diversity. Hump-shaped temporal dynamics can be obtained without speciation. Endemic species seem less susceptible to extinction than native non-endemic species. Endemism reflects stronger geographical and environmental isolation. Finally, only the integration of all implemented processes generates realistic spatio-temporal dynamics at population, species, community and assemblage levels.
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Climate-driven declines in arthropod abundance restructure a rainforest food web
Article
Full-text available
  • Oct 2018
Significance Arthropods, invertebrates including insects that have external skeletons, are declining at an alarming rate. While the tropics harbor the majority of arthropod species, little is known about trends in their abundance. We compared arthropod biomass in Puerto Rico’s Luquillo rainforest with data taken during the 1970s and found that biomass had fallen 10 to 60 times. Our analyses revealed synchronous declines in the lizards, frogs, and birds that eat arthropods. Over the past 30 years, forest temperatures have risen 2.0 °C, and our study indicates that climate warming is the driving force behind the collapse of the forest’s food web. If supported by further research, the impact of climate change on tropical ecosystems may be much greater than currently anticipated.
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Climatologies at high resolution for the earth's land surface areas
Article
Full-text available
  • Sep 2017
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better. Design Type(s) data integration objective • modeling and simulation objective Measurement Type(s) temperature of air • hydrological precipitation process Technology Type(s) data acquisition system Factor Type(s) Sample Characteristic(s) Earth • planetary atmosphere
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Evidence for mega-landslides as drivers of island colonization
Article
Full-text available
Aim How non-dispersive taxa colonize islands is generalized as being by wind, or rafting, with the implicit assumption that such events involve one (wind) or a few (rafting) individuals. However, because of the evolutionary time-scale for colonization events, the fit of individual species to a conceptual model of wind or rafting is difficult to assess. Here, we describe an alternative testable geologi- cal model for inter-island colonization that can result in larger effective found- ing population sizes than traditionally accepted colonization mechanisms. We then test for the fit of genetic data to this model using weevils from the Laparocerus tessellatus species complex. Location Canary Islands. Methods Using a combination of geological data for the Canary Islands, and mtDNA data from a weevil radiation within the Canary Islands, we test three species-level predictions for mega-landslides as drivers of oceanic rafting between islands and subsequent speciation: (1) colonization should involve multiple female lineages, (2) founding lineages should have a common geo- graphical origin, consistent with a mega-landslide event, and (3) colonization direction should be consistent with ocean currents. Results Both individual-level and population-level analyses support a mega- landslide event as the driver of colonization from the island of Tenerife to La Palma. At least four female lineages colonized La Palma from Tenerife, with the geographical range of ancestral sequences to these four lineages describing the limits of the La Orotava mega-landslide in Tenerife. Main conclusions In the context of island biogeographical theory, mega- landslides may be an important driver of colonization, and subsequent lineage diversification. They provide a framework for hypothesis testing using genetic data from species, or closely related species, with ranges that encompass land- slides and potential areas of colonization.
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Phylogenetic analysis of the genus Laparocerus, with comments on colonisation and diversification in Macaronesia (Coleoptera, Curculionidae, Entiminae)
Article
Full-text available
  • Feb 2017
The flightless Entiminae weevil genus Laparocerus is the species-richest genus, with 237 species and subspecies, inhabiting Macaronesia (Madeira archipelago, Selvagens, Canary Islands) and the continental ‘Macaronesian enclave’ in Morocco (one single polytypic species). This is the second contribution to gain insight of the genus and assist in its systematic revision with a mitochondrial phylogenetic analysis. It centres on the Canarian clade, adding the 12S rRNA gene to the combined set of COII and 16S rRNA used in our first contribution on the Madeiran clade (here re-analysed). The nuclear 28S rRNA was also used to produce an additional 4-gene tree to check coherency with the 3-gene tree. A total of 225 taxa (95%) has been sequenced, mostly one individual per taxa. Plausible explanations for incoherent data (mitochondrial introgressions, admixture, incomplete lineage sorting, etc.) are discussed for each of the monophyletic subclades that are coincident with established subgenera, or are restructured or newly described. The overall mean genetic divergence (p-distance) among species is 8.2%; the mean divergence within groups (subgenera) ranks from 2.9 to 7.0% (average 4.6%), and between groups, from 5.4% to 12.0% (average 9.2%). A trustful radiation event within a young island (1.72 Ma) was used to calibrate and produce a chronogram using the software RelTime. These results confirm the monophyly of both the Madeiran (36 species and subspecies) and the Canarian (196 species and subspecies) clades, which originated ca. 11.2 Ma ago, and started to radiate in their respective archipelagos ca. 8.5 and 7.7 Ma ago. The Madeiran clade seems to have begun in Porto Santo, and from there it jumped to the Desertas and to Madeira, with additional radiations. The Canarian clade shows a sequential star-shape radiation process generating subclades with a clear shift from East to West in coherence with the decreasing age of the islands. Laparocerus garretai from the Selvagens belongs to a Canarian subclade, and Laparocerus susicus from Morocco does not represent the ancestral continental lineage, but a back-colonisation from the Canaries to Africa. Dispersal processes, colonisation patterns, and ecological remarks are amply discussed. Diversification has been adaptive as well as non-adaptive, and the role of ’geological turbulence’ is highlighted as one of the principal drivers of intra-island allopatric speciation. Based on the phylogenetic results, morphological features and distribution, five new monophyletic subgenera are described: Aridotrox n. subg., Belicarius n. subg., Bencomius n. subg., Canariotrox n. subg., and Purpuranius n. subg, totalling twenty subgenera in Laparocerus.
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A topoclimate model for Quaternary insular speciation
Article
  • Oct 2019
Understanding the drivers of speciation within islands is key to explain the high levels of invertebrate diversification and endemism often observed within islands. Here, we propose an insular topoclimate model for Quaternary diversification (ITQD), and test the general prediction that, within a radially eroded conical island, glacial climate conditions facilitate the divergence of populations within species across valleys. Gran Canaria, Canary Islands. The Laparocerus tessellatus beetle species complex (Coleoptera, Curculionidae). We characterize individual‐level genomic relationships using single nucleotide polymorphisms produced by double‐digest restriction site associated DNA sequencing (ddRAD‐seq). A range of parameter values were explored in order to filter our data. We assess individual relatedness, species boundaries, demographic history and spatial patterns of connectivity. The total number of ddRAD‐seq loci per sample ranges from 4,576 to 512, with 11.12% and 4.84% of missing data respectively, depending on the filtering parameter combination. We consistently infer four genetically distinct ancestral populations and two presumed cases of admixture, one of which is largely restricted to high altitudes. Bayes factor delimitation support the hypothesis of four species, which is consistent with the four inferred ancestral gene pools. Landscape resistance analyses identified genomic relatedness among individuals in two out of the four inferred species to be best explained by annual precipitation during the last glacial maximum rather than geographic distance. Our data reveal a complex speciation history involving population isolation and admixture, with broad support for the ITQD model here proposed. We suggest that further studies are needed to test the generality of our model, and enrich our understanding of the evolutionary process in island invertebrates. Our results demonstrate the power of ddRAD‐seq data to provide a detailed understanding of the temporal and spatial dynamics of insular biodiversity.
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ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms
Article
Understanding how landscape features affect functional connectivity among populations is a cornerstone of spatial ecology and landscape genetic analyses. However, parameterization of resistance surfaces that best describe connectivity is a challenging and often subjective process. 2.ResistanceGA is an R package that utilizes a genetic algorithm to optimize resistance surfaces based on pairwise genetic data and effective distances calculated using CIRCUITSCAPE, least cost paths, or random-walk commute times. Functions in this package allow for the optimization of categorical and continuous resistance surfaces, and simultaneous optimization of multiple resistance surfaces. 3.ResistanceGA provides a coherent framework to optimize resistance surfaces without a priori assumptions, conduct model selection, and make inference about the contribution of each surface to total resistance. 4.ResistanceGA fills a void in the landscape genetic toolbox, allowing for unbiased optimization of resistance surfaces and for the simultaneous optimization of multiple resistance surfaces to create novel composite resistance surfaces, but could have broader applicability to other fields of spatial ecological research. This article is protected by copyright. All rights reserved.
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The true tempo of evolutionary radiation and decline revealed on the Hawaiian archipelago
Article
  • Mar 2017
Establishing the relationship between rates of change in species richness and biotic and abiotic environmental change is a major goal of evolutionary biology. Although exquisite fossil and geological records provide insight in rare cases, most groups lack high-quality fossil records. Consequently, biologists typically rely on molecular phylogenies to study the diversity dynamics of clades, usually by correlating changes in diversification rate with environmental or trait shifts. However, inferences drawn from molecular phylogenies can be limited owing to the challenge of accounting for extinct species, making it difficult to accurately determine the underlying diversity dynamics that produce them. Here, using a geologically informed model of the relationship between changing island area and species richness for the Hawaiian archipelago, we infer the rates of species richness change for 14 endemic groups over their entire evolutionary histories without the need for fossil data, or molecular phylogenies. We find that these endemic clades underwent evolutionary radiations characterized by initially increasing rates of species accumulation, followed by slow-downs. In fact, for most groups on most islands, their time of evolutionary expansion has long past, and they are now undergoing previously unrecognized long-term evolutionary decline. Our results show how landscape dynamism can drive evolutionary dynamics over broad timescales, including driving species loss that is not readily detected using molecular phylogenies, or without a rich fossil record. We anticipate that examination of other clades where the relationship between environmental change and species richness change can be quantified will reveal that many other living groups have also experienced similarly complex evolutionary trajectories, including long-term and ongoing evolutionary decline.
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A combined field survey and molecular identification protocol for comparing forest arthropod biodiversity across spatial scales
Article
  • Oct 2016
Obtaining fundamental biodiversity metrics such as alpha, beta and gamma diversity for arthropods is often complicated by a lack of prior taxonomic information and/or taxonomic expertise, which can result in unreliable morphologically based estimates. We provide a set of standardized ecological and molecular sampling protocols that can be employed by researchers whose taxonomic skills may be limited, and where there may be a lack of robust a priori information regarding the regional pool of species. These protocols combine mass sampling of arthropods, classification of samples into parataxonomic units (PUs), and selective sampling of individuals for mtDNA sequencing to infer biological species. We sampled ten lowland rainforest plots located on the volcanic oceanic island of Réunion (Mascarene archipelago) for spiders, a group with limited taxonomic and distributional data for this region. We classified adults and juveniles into PUs and then demonstrated the reconciliation of these units with presumed biological species using mtDNA sequence data, ecological data and distributional data. Because our species assignment protocol is not reliant upon prior taxonomic information, or taxonomic expertise, it minimises the problem of the Linnean shortfall to yield diversity estimates that can be directly compared across independent studies. Field sampling can be extended to other arthropod groups and habitats by adapting our field sampling protocol accordingly. This article is protected by copyright. All rights reserved.
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