Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non‐spatial regression

Bini, Luis Maurício; Diniz‐Filho, José Alexandre Felizola; Rangel, Thiago F.; Akre, Thomas S.; Albaladejo, Rafael G.; Albuquerque, Fábio Suzart de; Aparicio, Abelardo; Araújo, Miguel B.; Baselga, Andrés; Beck, Jan; Bellocq, M. Isabel; Böhning‐Gaese, Katrin; Borges, Paulo A. V.; Castro-Parga, Isabel; Khen, Chey Vun; Chown, Steven Loudon; Marco, Paulo De; Dobkin, David S.; Ferrer‐Castán, Dolores; Field, Richard; Filloy, Julieta; Fleishman, Erica; Gómez, José Luís Graña; Hortal, Joaquín; Iverson, John B.; Kerr, Jeremy T.; Kissling, W. Daniel; Kitching, Ian J.; León‐Cortés, Jorge L.; Lobo, Jorge M.; Montoya, Daniel; Morales‐Castilla, Ignacio; Moreno, Juan Carlos; Oberdorff, Thierry; Olalla‐Tárraga, Miguel Á.; Pausas, Juli G.; Qian, Hong; Rahbek, Carsten; Rodrı́guez, Miguel Á.; Rueda, Marta Pacheco; Ruggiero, Adriana; Sackmann, Paula; Sanders, Nathan J.; Terribile, Levi Carina; Vetaas, Ole R.; Hawkins, Bradford A.

doi:10.1111/j.1600-0587.2009.05717.x

Cited by 230 publications

(31 citation statements)

References 42 publications

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“…Because the data showed strong spatial autocorrelation, we fitted spatial error simultaneous autoregressive models (SARerr) to incorporate the autocorrelation structure of both phylogenetic structure metrics in the OLS models [50]. Although changes in coefficients between spatial and non-spatial methods may be largely idiosyncratic of the method used [51], SAR models has been empirically demonstrated to be among the best-performing methods for spatial data analysis in terms of Type I and Type II error rates [52]. Specifically, the SARerr model is preferable to other SAR models when dealing with spatially autocorrelated species distribution data as it generates more accurate estimates of regression model coefficients and performs well for all SAR model assumptions [50].…”

Section: (Iv) Regression Analysesmentioning

confidence: 99%

The building of a biodiversity hotspot across a land-bridge in the Mediterranean

et al. 2015

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Many of the macroevolutionary processes that have shaped present-day phylogenetic patterns were caused by geological events such as plate tectonics and temporary land-bridges. The study of spatial patterns of phylogenetic diversity can provide insights into these past events. Here we focus on a western Mediterranean biodiversity hotspot located in the southern Iberian Peninsula and northwest Africa, two regions that are separated by the Strait of Gibraltar. We explore the spatial structure of the phylogenetic relationships within and across large-scale plant assemblages. Significant turnover in terminal lineages tends to occur between landmasses, whereas turnover in deep lineages tends to occur within landmasses. Plant assemblages in the western ecoregions of this hotspot tend to be phylogenetically overdispersed but are phylogenetically clustered on its eastern margins. We discuss our results in the light of potential scenarios of niche evolution (or conservatism) and lineage diversification. The significant turnover between landmasses suggests a common scenario of allopatric speciation that could have been facilitated by the intermittent joining of the two continents. This may have constituted an important stimulus for diversification and the emergence of this western Mediterranean biodiversity hotspot.

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Section: (Iv) Regression Analysesmentioning

confidence: 99%

The building of a biodiversity hotspot across a land-bridge in the Mediterranean

et al. 2015

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“…An important confounding variable to understanding patterns of species richness variation is its intrinsic spatial autocorrelation, which may generate biased significance levels in statistical analysis (Bini et al 2009). As our main interest here is evaluating the predictive power of genera and Libellulidae richness on the total odonate species and present distribution of the groups and of the similarity between the processes that affect species richness in distant and similar taxa.…”

Section: Discussionmentioning

confidence: 99%

Higher-Taxon and Cross-Taxon Surrogates for Odonate Biodiversity in Brazil

Vianna¹,

Marco²

2012

Nat. Conserv.

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Odonate distributional patterns have recently become a focus of a global biodiversity evaluation, but it may present large gaps in biogeographical information, especially in tropical areas, which suggests the need of a surrogate approach for setting conservation priorities. Here we assemble available information of distribution of Brazilian odonate species and try to evaluate two different surrogate possibilities: i) a higher-taxon approach based on genera richness, and ii) a cross-taxa approach using the larger-sized Libellulidae species. The species richness distribution pattern shows a bias toward areas near research centers or with easy accessibility. Only 29% of the territory had any distributional information about odonates. A higher association of genera richness and species richness was observed and remained high even after controlling for differences in sampling effort. Libellulidae species richness was also a good surrogate, despite the low cover of available information of Brazilian territory. Our results support the use of higher-taxa over other approaches but highlight the importance of intensify sampling especially at the Cerrado, Caatinga and Amazonian biomes.

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“…Conservation biologists want to use the most powerful method, and recent studies of spatial analyses conclude that applying some of the techniques they describe is better than doing nothing (Dormann et al 2007;Bini et al 2009;Beale et al 2010). However, conservation biologists must be clear about their objectives.…”

Section: Discussionmentioning

confidence: 99%

“…Discussion of the points alluded to in the preceding paragraphs (mainly the one related to coefficient shifts) is recent, and filled with controversies (Lennon 2000;DinizFilho et al 2003;Hawkins et al 2007;Bini et al 2009). The second class of problems has been the focus of most of the recent discussions in the literature (Diniz-Filho et al 2003;Dormann et al 2007;Beguería & Pueyo 2009;Bini et al 2009), but these aspects are also related to the practice of partitioning variance between interesting predictor variables and the possibly confounding factor "space" (Borcard et al 1992;Legendre 1993;Legendre & Legendre 1998).…”

Section: Why Undertake Spatial Analyses?mentioning

confidence: 99%

“…The second class of problems has been the focus of most of the recent discussions in the literature (Diniz-Filho et al 2003;Dormann et al 2007;Beguería & Pueyo 2009;Bini et al 2009), but these aspects are also related to the practice of partitioning variance between interesting predictor variables and the possibly confounding factor "space" (Borcard et al 1992;Legendre 1993;Legendre & Legendre 1998). Partitioning variance between "space" and ecological predictor variables is the focus of research on niche versus neutral models of community dynamics (Peres-Neto et al 2006;Legendre et al 2009aLegendre et al , 2009bPeres-Neto & Legendre 2010), and the question of whether area or habitat is more important for reserve design.…”

Section: Why Undertake Spatial Analyses?mentioning

confidence: 99%

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The Geometry of Spatial Analyses: Implications for Conservation Biologists

Landeiro¹,

Magnusson²

2011

NatCon

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Most conservation biology is about the management of space and therefore requires spatial analyses. However, recent debates in the literature have focused on a limited range of issues related to spatial analyses that are not always of primary interest to conservation biologists, especially autocorrelation and spatial confounding. Explanations of how these analyses work, and what they do, are permeated with mathematical formulas and statistical concepts that are outside the experience of most working conservationists. Here, we describe the concepts behind these analyses using simple simulations to exemplify their main goals, functions and assumptions, and graphically illustrate how processes combine to generate common spatial patterns. Understanding these concepts will allow conservation biologists to make better decisions about the analyses most appropriate for their problems.

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Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non‐spatial regression

Cited by 230 publications

References 42 publications

The building of a biodiversity hotspot across a land-bridge in the Mediterranean

The building of a biodiversity hotspot across a land-bridge in the Mediterranean

Higher-Taxon and Cross-Taxon Surrogates for Odonate Biodiversity in Brazil

The Geometry of Spatial Analyses: Implications for Conservation Biologists

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