2017
DOI: 10.1007/s00442-017-3967-4
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Temporal changes in bird functional diversity across the United States

Abstract: Global changes are modifying the structure of species assemblages, but the generality of resulting diversity patterns and of their drivers is poorly understood. Any such changes can be detected and explained by comparing temporal trends in taxonomic and functional diversity over broad spatial extents. In this study, we addressed three complementary questions: How did bird taxonomic and functional diversity change over the past 40 years in the conterminous United States? Are these trends non-linear? Can tempora… Show more

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Cited by 35 publications
(46 citation statements)
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“…Although migration is well studied from individual and population perspectives, the communities that are composed by migrant and sedentary species have received little attention from the functional perspective. Thus, examining changes in the functional structure of waterbird seasonal communities over time (both within and between years, and between seasons) can help us better understand the dynamics of these communities (Barnagaud et al 2017).…”
Section: Introductionmentioning
confidence: 99%
“…Although migration is well studied from individual and population perspectives, the communities that are composed by migrant and sedentary species have received little attention from the functional perspective. Thus, examining changes in the functional structure of waterbird seasonal communities over time (both within and between years, and between seasons) can help us better understand the dynamics of these communities (Barnagaud et al 2017).…”
Section: Introductionmentioning
confidence: 99%
“…The first two groups contributed to a weakening of the relationship, whereas the third strengthened the relationship, which might explain the stabilization of the strength of the relationship since c . 1990 (Barnagaud, Gaüzère, Zuckerberg, Princé, & Svenning, ) (Figure ). Increasing inter‐annual temperature variation had the strongest negative impact on specialization and positive impact on richness (Gilchrist, ).…”
Section: Discussionmentioning
confidence: 99%
“…We identified three main groups of variables that drove temporal change in the richness-specialization relationship and in the spatial covariation of richness and specialization. The first two groups contributed to a weakening of the relationship, whereas the third strengthened the relationship, which might explain the stabilization of the strength of the relationship since c. 1990 (Barnagaud, Gaüzère, Zuckerberg, Princé, & Svenning, 2017) (Figure 4).…”
Section: Drivers Of a Weakening Richness-specialization Relationshimentioning
confidence: 99%
“…We compiled 16 ecological and life‐history traits for the bird species recorded at each of the three states on the basis of the traits data set published by Barnagaud et al. () and followed the same methodology to compile data for the additional species present in our data sets (Appendix : Table S1). The (categorical and quantitative) functional traits used in this analysis are grouped into three categories: (1) ecological traits (primary habitat, nest location, nest type, foraging strategy, main diet) reflecting the extent to which species overlap in their use of habitat and dietary resources; (2) life‐history traits (territoriality, migration, developmental mode, log‐transformed body mass, wingspan, body length) that separate sedentary species with large body sizes and large home ranges from smaller species with smaller territories; and (3) reproductive traits (number of broods per year, clutch size, mating, chick‐rearing mode, maximum life span) that are related to breeding performances and duration of life cycle (Barnagaud et al.…”
Section: Methodsmentioning
confidence: 99%
“…We compiled 16 ecological and life-history traits for the bird species recorded at each of the three states on the basis of the traits data set published by Barnagaud et al (2017) and followed the same methodology to compile data for the additional species present in our data sets (Appendix S1: Table S1). The (categorical and quantitative) functional traits used in this analysis are grouped into three categories: (1)…”
Section: Functional Traitsmentioning
confidence: 99%