Deconstructing richness patterns by commonness and rarity reveals bioclimatic and spatial effects in black fly metacommunities
Summary Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns. In this...
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| Vydané v: | Freshwater biology Ročník 61; číslo 6; s. 923 - 932 |
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Blackwell Publishing Ltd
01.06.2016
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| Abstract | Summary
Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns.
In this study, we addressed the relative role of spatial and large‐scale bioclimatic variables along a commonness and rarity gradient using Simuliidae (Diptera) species richness. A database of species occurrences at 459 locations in Brazil was used to estimate the distribution of 58 simuliid species. Total species richness at each location was estimated first using all occurrences and then by removing one species at a time, following a commonest to rarest gradient (CtR) and vice‐versa (RtC). Partial regression analysis was used to test the influence of sets of bioclimatic (E) and spatial (S) variables for Simuliidae species richness across both CtR and RtC gradients.
In the CtR gradient, the pure spatial component alone explained between 40% and 60% of the variation in simuliid richness when the total number of species was greater than ˜35. After removal of the 35th most common species, the model fit decreased sharply reaching nearly zero when only rare species were present. Variation explained by the shared component E + S decreased continuously along the CtR gradient. The relative role of predictor variables on the RtC gradient was similar to CtR gradient. However, removing the rare species first did not change which components best explained species richness.
Our gradual deconstructive approach revealed that common species contribute more to species richness variation than rare species, and that the role of predictors in explaining this pattern cannot be untangled by analysing richness of rare and common species in a categorical way. |
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| AbstractList | Summary Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns. In this study, we addressed the relative role of spatial and large-scale bioclimatic variables along a commonness and rarity gradient using Simuliidae (Diptera) species richness. A database of species occurrences at 459 locations in Brazil was used to estimate the distribution of 58 simuliid species. Total species richness at each location was estimated first using all occurrences and then by removing one species at a time, following a commonest to rarest gradient (CtR) and vice-versa (RtC). Partial regression analysis was used to test the influence of sets of bioclimatic (E) and spatial (S) variables for Simuliidae species richness across both CtR and RtC gradients. In the CtR gradient, the pure spatial component alone explained between 40% and 60% of the variation in simuliid richness when the total number of species was greater than 35. After removal of the 35th most common species, the model fit decreased sharply reaching nearly zero when only rare species were present. Variation explained by the shared component E + S decreased continuously along the CtR gradient. The relative role of predictor variables on the RtC gradient was similar to CtR gradient. However, removing the rare species first did not change which components best explained species richness. Our gradual deconstructive approach revealed that common species contribute more to species richness variation than rare species, and that the role of predictors in explaining this pattern cannot be untangled by analysing richness of rare and common species in a categorical way. 1. Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns. 2. In this study, we addressed the relative role of spatial and large-scale bioclimatic variables along a commonness and rarity gradient using Simuliidae (Diptera) species richness. A database of species occurrences at 459 locations in Brazil was used to estimate the distribution of 58 simuliid species. Total species richness at each location was estimated first using all occurrences and then by removing one species at a time, following a commonest to rarest gradient (CtR) and vice-versa (RtC). Partial regression analysis was used to test the influence of sets of bioclimatic (E) and spatial (S) variables for Simuliidae species richness across both CtR and RtC gradients. 3. In the CtR gradient, the pure spatial component alone explained between 40% and 60% of the variation in simuliid richness when the total number of species was greater than ~35. After removal of the 35th most common species, the model fit decreased sharply reaching nearly zero when only rare species were present. Variation explained by the shared component E + S decreased continuously along the CtR gradient. The relative role of predictor variables on the RtC gradient was similar to CtR gradient. However, removing the rare species first did not change which components best explained species richness. 4. Our gradual deconstructive approach revealed that common species contribute more to species richness variation than rare species, and that the role of predictors in explaining this pattern cannot be untangled by analysing richness of rare and common species in a categorical way. Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns. In this study, we addressed the relative role of spatial and large‐scale bioclimatic variables along a commonness and rarity gradient using Simuliidae (Diptera) species richness. A database of species occurrences at 459 locations in Brazil was used to estimate the distribution of 58 simuliid species. Total species richness at each location was estimated first using all occurrences and then by removing one species at a time, following a commonest to rarest gradient (CtR) and vice‐versa (RtC). Partial regression analysis was used to test the influence of sets of bioclimatic (E) and spatial (S) variables for Simuliidae species richness across both CtR and RtC gradients. In the CtR gradient, the pure spatial component alone explained between 40% and 60% of the variation in simuliid richness when the total number of species was greater than ˜35. After removal of the 35th most common species, the model fit decreased sharply reaching nearly zero when only rare species were present. Variation explained by the shared component E + S decreased continuously along the CtR gradient. The relative role of predictor variables on the RtC gradient was similar to CtR gradient. However, removing the rare species first did not change which components best explained species richness. Our gradual deconstructive approach revealed that common species contribute more to species richness variation than rare species, and that the role of predictors in explaining this pattern cannot be untangled by analysing richness of rare and common species in a categorical way. Summary Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns. In this study, we addressed the relative role of spatial and large‐scale bioclimatic variables along a commonness and rarity gradient using Simuliidae (Diptera) species richness. A database of species occurrences at 459 locations in Brazil was used to estimate the distribution of 58 simuliid species. Total species richness at each location was estimated first using all occurrences and then by removing one species at a time, following a commonest to rarest gradient (CtR) and vice‐versa (RtC). Partial regression analysis was used to test the influence of sets of bioclimatic (E) and spatial (S) variables for Simuliidae species richness across both CtR and RtC gradients. In the CtR gradient, the pure spatial component alone explained between 40% and 60% of the variation in simuliid richness when the total number of species was greater than ˜35. After removal of the 35th most common species, the model fit decreased sharply reaching nearly zero when only rare species were present. Variation explained by the shared component E + S decreased continuously along the CtR gradient. The relative role of predictor variables on the RtC gradient was similar to CtR gradient. However, removing the rare species first did not change which components best explained species richness. Our gradual deconstructive approach revealed that common species contribute more to species richness variation than rare species, and that the role of predictors in explaining this pattern cannot be untangled by analysing richness of rare and common species in a categorical way. Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving variation in biological communities. Such an approach considers differences among organisms and emergent ecological patterns. In this study, we addressed the relative role of spatial and large‐scale bioclimatic variables along a commonness and rarity gradient using Simuliidae (Diptera) species richness. A database of species occurrences at 459 locations in Brazil was used to estimate the distribution of 58 simuliid species. Total species richness at each location was estimated first using all occurrences and then by removing one species at a time, following a commonest to rarest gradient (CtR) and vice‐versa (RtC). Partial regression analysis was used to test the influence of sets of bioclimatic (E) and spatial (S) variables for Simuliidae species richness across both CtR and RtC gradients. In the CtR gradient, the pure spatial component alone explained between 40% and 60% of the variation in simuliid richness when the total number of species was greater than ˜35. After removal of the 35th most common species, the model fit decreased sharply reaching nearly zero when only rare species were present. Variation explained by the shared component E + S decreased continuously along the CtR gradient. The relative role of predictor variables on the RtC gradient was similar to CtR gradient. However, removing the rare species first did not change which components best explained species richness. Our gradual deconstructive approach revealed that common species contribute more to species richness variation than rare species, and that the role of predictors in explaining this pattern cannot be untangled by analysing richness of rare and common species in a categorical way. |
| Author | Menezes, Jorge F. S. Valente-Neto, Francisco Siqueira, Tadeu Pepinelli, Mateus Zampiva, Nayara K. Roque, Fabio de O. Hamada, Neusa Swan, Christopher |
| Author_xml | – sequence: 1 givenname: Fabio de O. surname: Roque fullname: Roque, Fabio de O. email: Correspondence: Fabio de O. Roque, Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul, CEP 79070-900, Campo Grande, Mato Grosso do Sul, Brazil., roque.eco@gmail.com organization: Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil – sequence: 2 givenname: Nayara K. surname: Zampiva fullname: Zampiva, Nayara K. organization: Programa de Pós-graduação em Entomologia e Conservação da Biodiversidade, Universidade Federal da Grande Dourados, Mato Grosso do Sul, Dourados, Brazil – sequence: 3 givenname: Francisco surname: Valente-Neto fullname: Valente-Neto, Francisco organization: Programa de Pós-Graduação em Ecologia e Conservação, Universidade Federal de Mato Grosso do Sul, Mato Grosso do Sul, Campo Grande, Brazil – sequence: 4 givenname: Jorge F. S. surname: Menezes fullname: Menezes, Jorge F. S. organization: Marco and Louise Mitrani Department of Desert Ecology, The Swiss Institute for Dryland Environmental & Energy Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel – sequence: 5 givenname: Neusa surname: Hamada fullname: Hamada, Neusa organization: Instituto Nacional de Pesquisas da Amazônia, Amazonas, Manaus, Brazil – sequence: 6 givenname: Mateus surname: Pepinelli fullname: Pepinelli, Mateus organization: Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada – sequence: 7 givenname: Tadeu surname: Siqueira fullname: Siqueira, Tadeu organization: Departamento de Ecologia, Instituto de Biociências, UNESP - Universidade Estadual Paulista, SP, Rio Claro, Brazil – sequence: 8 givenname: Christopher surname: Swan fullname: Swan, Christopher organization: University of Maryland, Baltimore County, MD, Baltimore, USA |
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(e_1_2_6_20_1) 2001; 16 Pepinelli M. (e_1_2_6_48_1) 2005; 1040 e_1_2_6_12_1 Adler P.H. (e_1_2_6_3_1) 2004 e_1_2_6_18_1 e_1_2_6_39_1 e_1_2_6_56_1 e_1_2_6_16_1 Marquet P.A. (e_1_2_6_36_1) 2004 e_1_2_6_37_1 e_1_2_6_58_1 e_1_2_6_63_1 e_1_2_6_42_1 Hamada N. (e_1_2_6_22_1) 2014 e_1_2_6_21_1 e_1_2_6_40_1 e_1_2_6_61_1 e_1_2_6_8_1 e_1_2_6_4_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_29_1 e_1_2_6_44_1 e_1_2_6_27_1 e_1_2_6_46_1 R Core Team (e_1_2_6_51_1) 2014 |
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Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes... Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes driving... Summary Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes... 1. Deconstructing biological communities by grouping species according to their commonness or rarity might improve our understanding about the processes... |
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| SubjectTerms | bioclimatic indexes Bioclimatology Brazil Diptera Freshwater Neotropical streams niche processes Rare species rarity Regression analysis Simuliidae species diversity Species richness stochasticity |
| Title | Deconstructing richness patterns by commonness and rarity reveals bioclimatic and spatial effects in black fly metacommunities |
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