Exotic flower visitors exploit large floral trait spaces resulting in asymmetric resource partitioning with native visitors

Abstract

Exotic species often cause severe alterations in native communities due to their ability to rapidly and efficiently utilize a broad spectrum of resources. In flower–visitor interactions, the breadth of resource use by native and exotic animals as well as the partitioning of resources among them is often estimated based on the number of (shared) plant species used as resources. However, whether a flower visitor is able to exploit plant resources has been shown to be delimited by functional floral traits such as morphological barriers or attractive or repellent chemical cues. Each of the ecologically relevant traits can be viewed as a dimension of a Hutchinsonian n‐dimensional hypervolume, which characterizes the range of phenotypes exploitable by a species. In this study, we quantified the sizes and overlaps of n‐dimensional hypervolumes defined by floral traits that are exploited by native and exotic flower visitors (afterwards referred to as exploited space, ES). In the heavily invaded Hawaii Volcanoes National Park, USA, we phenotyped 40 native and exotic plant species and recorded flower–visitor interactions. To quantify the size and overlap of ES, we applied dynamic range boxes. On average, exotic flower visitors were more generalized in resource use (larger ES) than natives ones, which is additionally indicated by the absence of native flower visitors on exotic flowering plant species. In particular, ES based on floral scent emission was larger for exotic flower visitors compared to native ones. The unevenly expanded ES of native and exotic animals led to an asymmetric overlap of floral ES where exotic flower visitors shared only a small proportion of their ES with natives but occupied a large proportion of the ES of natives. The asymmetry in resource use of native and exotic flower visitors suggests a potential advantage in resource exploitation of the latter, potentially explaining their success in Hawaiian ecosystems. Predicted range expansion of exotic plant and animal species may further increase the competition for and reduce the availability of resources for native animals. This may lead to further declines of native species and increasing threats for Hawaiian ecosystems. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12932/suppinfo is available for this article.

Elevation predicts the functional composition of alpine plant communities based on vegetative traits, but not based on floral traits

“…Furthermore, using dynRB, the abundance of plant species can be considered. We defined the trait space of communities as the n-dimensional hypervolume occupied by the plant species j present in a plot p weighted by the abundance of the plant species j. Abundance a pj was first transformed by a pj ′ = log(a pj + 1) × 10, and then, a pj ′ was rounded to the nearest integer r(a pj ′), which is required by dynRB to consider plant abundance (compare to Kuppler et al 2017). Each value of trait t of plant species j was r(a pj ′) times added to the vector defining the functional position of the plant community in plot p. Thus, for each plot p and trait t, we obtained a vector with the length…”

“…The framework of n-dimensional hypervolumes considers, unlike other multivariate approaches, each trait equally, does not reduce the number of dimensions, and thus represents a direct representation of functional community composition (Barros et al 2016;Carmona et al 2016;Junker et al 2016;Kuppler et al 2017;Lamanna et al 2014). Accordingly, hypervolumes have been shown to be a valuable approach to track changes in community composition (Barros et al 2016).…”

Biotic interactions in species distribution models enhance model performance and shed light on natural history of rare birds: a case study using the straight‐billed reedhaunter Limnoctites rectirostris

“…In this sense, SDMs have addressed a wide array of questions, such as quantifying environmental niches of species and predicting their geographic distributions, assessing the impact of global environmental change on species distributions, predicting suitable areas for rare or endangered species, and supporting appropriate conservation planning (Guisan and Thuiller ). In this field of ecology, birds have been the focus of many SDM studies because of the high availability of freely accessible avian occurrence data (Engler et al ).…”

“…Indeed, most SDMs typically ignore the effects of biotic interactions (Guisan and Thuiller , Elith and Leathwick ), mainly as a result of two factors: 1) poor knowledge on biotic interactions at large spatial scales, and 2) assumptions under the Eltonian noise hypothesis, which states that abiotic factors, such as climatic variables, are the only drivers limiting species distributions at large spatial scales and low resolution, whereas biotic interactions would act at smaller spatial scales and higher resolution (Soberón and Nakamura , Boulangeat et al ). Nevertheless, both theory and facts contest such statement because quantifying the fundamental niche does not explain the entire distribution for every species, and numerous studies have shown that the inclusion of biotic interactions improve predictions of species distributions at broad geographical scales (Araújo and Luoto , Heikkinen et al , Bateman et al , Hof et al , Giannini et al , Araújo and Rozenfeld , Araújo et al , Crystal‐Ornelas et al , Atauchi et al ). Besides, endotherm distributions may be less directly linked to bioclimatic variables than ectotherm distributions (Engler et al ).…”

“…Nevertheless, both theory and facts contest such statement because quantifying the fundamental niche does not explain the entire distribution for every species, and numerous studies have shown that the inclusion of biotic interactions improve predictions of species distributions at broad geographical scales (Araújo and Luoto , Heikkinen et al , Bateman et al , Hof et al , Giannini et al , Araújo and Rozenfeld , Araújo et al , Crystal‐Ornelas et al , Atauchi et al ). Besides, endotherm distributions may be less directly linked to bioclimatic variables than ectotherm distributions (Engler et al ). Therefore, a critical assessment about the importance of biotic interactions in SDMs, particularly in endotherms, is still needed.…”