How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems

Zhang, Chongliang; Chen, Yong; Xu, Binduo; Xue, Ying; Ren, Yiping

doi:10.1111/ddi.12970

Cited by 15 publications

(8 citation statements)

References 78 publications

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“…In contrast, the approach outlined here follows an 'assemble first, predict later' strategy (Ferrier and Guisan 2006), as we first calculate an assemblage-level metric (species turnover) and then model it directly as a function environmental predictors. While the quality of predictions still depends on how well the model and the input data capture general trends underlying species turnover, comparative studies have shown that assemblage-level approaches consistently outperform species-level approaches when predicting alpha and beta diversity (Zhang et al 2019). Moreover, predicting source regions from a model of species turnover does not require floristic data for all of the investigated geographical regions, which makes it less data-intensive than species-level approaches (Graves and Rahbek 2005) while offering much finer spatial resolutions than purely checklist-based methods (Papadopulos et al 2011).…”

Section: Turnover-based Source Pool Estimationmentioning

confidence: 99%

Source pools and disharmony of the world's island floras

et al. 2020

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Island disharmony refers to the biased representation of higher taxa on islands compared to their mainland source regions and represents a central concept in island biology. Here, we develop a generalizable framework for approximating these source regions and conduct the first global assessment of island disharmony and its underlying drivers. We compiled vascular plant species lists for 178 oceanic islands and 735 mainland regions. Using mainland data only, we modelled species turnover as a function of environmental and geographic distance and predicted the proportion of shared species between each island and mainland region. We then quantified the over-or under-representation of families on individual islands (representational disharmony) by contrasting the observed number of species against a null model of random colonization from the mainland source pool, and analysed the effects of six family-level functional traits on the resulting measure. Furthermore, we aggregated the values of representational disharmony per island to characterize overall taxonomic bias of a given flora (compositional disharmony), and analysed this second measure as a function of four island biogeographical variables. Our results indicate considerable variation in representational disharmony both within and among plant families. Examples of generally over-represented families include Urticaceae, Convolvulaceae and almost all pteridophyte families. Other families such as Asteraceae and Orchidaceae were generally under-represented, with local peaks of over-representation in known radiation hotspots. Abiotic pollination and a lack of dispersal specialization were most strongly associated with an insular over-representation of families, whereas other family-level traits showed minor effects. With respect to compositional disharmony, large, high-elevation islands

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Section: Turnover-based Source Pool Estimationmentioning

confidence: 99%

Source pools and disharmony of the world's island floras

et al. 2020

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“…Boonman et al, 2020; Niittynen et al, 2020). Yet, this dichotomy has long been recognized in the context of modelling species distributions and species richness (Baselga & Araújo, 2010; Calabrese et al, 2014; D'Amen et al, 2017; Dubuis et al, 2011; Zhang et al, 2019; Zurell et al, 2016). In their review, Ferrier and Guisan (2006) distinguished the ‘assemble‐first, predict‐later’ from the ‘predict‐first, assemble‐later’, which echoes the two approaches described above.…”

Section: Introductionmentioning

confidence: 99%

Predict first–assemble later versus assemble first–predict later: Revisiting the dilemma for functional biogeography

Deschamps,

Poggiato,

Brun

et al. 2023

Methods Ecol Evol

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Predicting community‐level trait indices, such as community‐weighted mean or functional dispersion, in space or time, is crucial, as they are markers of ecosystem functions, proxies for ecosystem services, and are now integral part of conservation planning. On one hand, since members of a species share similar traits, a natural way is to model species distributions as a function of the environment first (i.e. with species distribution models), and then to reconstruct trait indices for any locality of interest (predict‐first). On the other hand, community‐level trait indices can be seen as the direct result of environmental filtering, and thus their distributions may directly be modelled in response to the environment (assemble‐first). Although relatively different, these two approaches have been used interchangeably in trait‐based ecology with unknown consequences on their usability. Here, using plant community (4463 plots) and trait data (LNC, leaf nitrogen content; PLH, plant height; SLA, specific leaf area, for >800 species) covering the French Alps, we compared the two approaches to predict community mean and functional dispersion indices, accounting or not for abundance (CM, community mean; CWM, community weighted mean; FDis, functional dispersion; uFDis, unweighted functional dispersion). We tested both interpolation versus extrapolation capabilities of the two approaches. To support the empirical findings, we also run the same comparative analysis on simulated community data. While both approaches produced similar and skillful CM/CWM predictions (R2 > 0.63 for CWM PLH) for interpolation, the assemble‐first outperformed the predict‐first approach for extrapolation to unobserved environmental conditions (R2 = 0.60 against 0.54 for CWM PLH). Functional dispersion was generally less well predicted, although the assemble‐first approach again provided better predictions for both interpolation (R2 = 0.31 against 0.27 for FDis PLH) and extrapolation (R2 = 0.28 against 0.18 for FDis PLH). The predict‐first approach systematically overestimated FDis. Results were confirmed by the simulation experiments. We conclude that the direct modelling of community‐level indices provides more robust spatial predictions over space and time and should thus be preferred. This approach also does not suffer from having generally too many species predicted by the predict‐first approach.

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“…However, resolving the contribution to biodiversity patterns of environmental factors acting at a regional scale is, to date, essential to the implementation of effective conservation strategies in the face of e.g. deforestation and climate change ( [25,26] and literature cited). Accordingly, several attempts have been made to model biodiversity of freshwater environments by means of regional bioclimatic factors [27,28].…”

Section: Introductionmentioning

confidence: 99%

Species Richness and Taxonomic Distinctness of Zooplankton in Ponds and Small Lakes from Albania and North Macedonia: The Role of Bioclimatic Factors

Mancinelli

Mali

Belmonte

2019

Water

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Resolving the contribution to biodiversity patterns of regional-scale environmental drivers is, to date, essential in the implementation of effective conservation strategies. Here, we assessed the species richness S and taxonomic distinctness Δ+ (used a proxy of phylogenetic diversity) of crustacean zooplankton assemblages from 40 ponds and small lakes located in Albania and North Macedonia and tested whether they could be predicted by waterbodies’ landscape characteristics (area, perimeter, and altitude), together with local bioclimatic conditions that were derived from Wordclim and MODIS databases. The results showed that a minimum adequate model, including the positive effects of non-arboreal vegetation cover and temperature seasonality, together with the negative influence of the mean temperature of the wettest quarter, effectively predicted assemblages’ variation in species richness. In contrast, taxonomic distinctness did not predictably respond to landscape or bioclimatic factors. Noticeably, waterbodies’ area showed a generally low prediction power for both S and Δ+. Additionally, an in-depth analysis of assemblages’ species composition indicated the occurrence of two distinct groups of waterbodies characterized by different species and different precipitation and temperature regimes. Our findings indicated that the classical species-area relationship hypothesis is inadequate in explaining the diversity of crustacean zooplankton assemblages characterizing the waterbodies under analysis. In contrast, local bioclimatic factors might affect the species richness and composition, but not their phylogenetic diversity, the latter likely to be influenced by long-term adaptation mechanisms.

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How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems

Cited by 15 publications

References 78 publications

Source pools and disharmony of the world's island floras

Source pools and disharmony of the world's island floras

Predict first–assemble later versus assemble first–predict later: Revisiting the dilemma for functional biogeography

Species Richness and Taxonomic Distinctness of Zooplankton in Ponds and Small Lakes from Albania and North Macedonia: The Role of Bioclimatic Factors

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