Browsing by Author "Gillespie, Rosemary G."
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- Collective and harmonized high throughput barcoding of insular arthropod biodiversity: Toward a Genomic Observatories Network for islandsPublication . Emerson, Brent C.; Borges, Paulo A. V.; Cardoso, Pedro; Convey, Peter; deWaard, Jeremy R.; Economo, Evan P.; Gillespie, Rosemary G.; Kennedy, Susan; Krehenwinkel, Henrik; Meier, Rudolf; Roderick, George K.; Strasberg, Dominique; Thébaud, Christophe; Traveset, Anna; Creedy, Thomas J.; Meramveliotakis, Emmanouil; Noguerales, Víctor; Overcast, Isaac; Morlon, Hélène; Papadopoulou, Anna; Vogler, Alfried P.; Arribas, Paula; Andújar, CarmeloABSTRACT: 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, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could 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 identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.
- Topography-driven isolation, speciation and a global increase of endemism with elevationPublication . Steinbauer, Manuel J.; Field, Richard; Grytnes, John-Arvid; Trigas, Panayiotis; Ah-Peng, Claudine; Attorre, Fabio; Birks, H. John B.; Borges, Paulo A. V.; Cardoso, Pedro; Chou, Chang-Hung; De Sanctis, Michele; Sequeira, Miguel M.; Duarte, Maria C.; Elias, Rui B.; Fernández-Palacios, José María; Gabriel, Rosalina; Gereau, Roy E.; Gillespie, Rosemary G.; Greimler, Josef; Harter, David E. V.; Huang, Tsurng-Juhn; Irl, Severin D. H.; Jeanmonod, Daniel; Jentsch, Anke; Jump, Alistair S.; Kueffer, Christoph; Nogué, Sandra; Otto, Rüdiger; Price, Jonathan; Romeiras, Maria M.; Strasberg, Dominique; Stuessy, Tod; Svenning, Jens-Christian; Vetaas, Ole R.; Beierkuhnlein, CarlAIM: Higher-elevation areas on islands and continental mountains tend to be separated by longer distances, predicting higher endemism at higher elevations; our study is the first to test the generality of the predicted pattern. We also compare it empirically with contrasting expectations from hypotheses invoking higher speciation with area, temperature and species richness. Location Thirty-two insular and 18 continental elevational gradients from around the world. Methods We compiled entire floras with elevation-specific occurrence information, and calculated the proportion of native species that are endemic (‘percent endemism’) in 100-m bands, for each of the 50 elevational gradients. Using generalized linear models, we tested the relationships between percent endemism and elevation, isolation, temperature, area and species richness. RESULTS: Percent endemism consistently increased monotonically with elevation, globally. This was independent of richness–elevation relationships, which had varying shapes but decreased with elevation at high elevations. The endemism–elevation relationships were consistent with isolation-related predictions, but inconsistent with hypotheses related to area, richness and temperature. Main conclusions Higher per-species speciation rates caused by increasing isolation with elevation are the most plausible and parsimonious explanation for the globally consistent pattern of higher endemism at higher elevations that we identify. We suggest that topography-driven isolation increases speciation rates in mountainous areas, across all elevations and increasingly towards the equator. If so, it represents a mechanism that may contribute to generating latitudinal diversity gradients in a way that is consistent with both present-day and palaeontological evidence.
- A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communitiesPublication . Overcast, Isaac; Ruffley, Megan; Rosindell, James; Harmon, Luke; Borges, Paulo A. V.; Emerson, Brent C.; Etienne, Rampal S.; Gillespie, Rosemary G.; Krehenwinkel, Henrik; Mahler, D. Luke; Massol, Francois; Parent, Christine E.; Patiño, Jairo; Peter, Ben; Week, Bob; Wagner, Catherine; Hickerson, Michael J.; Rominger, AndrewBiodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community-scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.