Dataset Information

Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients.

ABSTRACT: A fundamental goal of ecology is to understand the determinants of species' distributions (i.e., the set of locations where a species is present). Competition among species (i.e., interactions among species that harms each of the species involved) is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large-scale effects of competition or other biotic interactions is to fit species' distributions models (SDMs) and assess the effect of competitors on the distribution and abundance of the species of interest. It is often difficult to validate the accuracy of this approach with available data. Here, we simulate virtual species that experience competition. In these simulated datasets, we can unambiguously identify the effects that competition has on a species' distribution. We then fit SDMs to the simulated datasets and test whether we can use the outputs of the SDMs to infer the true effect of competition in each simulated dataset. In our simulations, the abiotic environment influenced the effects of competition. Thus, our SDMs often inferred that the abiotic environment was a strong predictor of species abundance, even when the species' distribution was strongly affected by competition. The severity of this problem depended on whether the competitor excluded the focal species from highly suitable sites or marginally suitable sites. Our results highlight how correlations between biotic interactions and the abiotic environment make it difficult to infer the effects of competition using SDMs.

SUBMITTER: Godsoe W

PROVIDER: S-EPMC5243196 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients.

Godsoe William W Franklin Janet J Blanchet F Guillaume FG

Ecology and evolution 20161220 2

A fundamental goal of ecology is to understand the determinants of species' distributions (i.e., the set of locations where a species is present). Competition among species (i.e., interactions among species that harms each of the species involved) is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large-scale effects of competition or other biotic interactions is to fit species' distributions models (SDMs) and as ...[more]

PMID: 28116060

Similar Datasets

Project description:Many metagenomic sequencing studies have observed the presence of closely related bacterial species or genotypes in the same microbiome. Previous attempts to explain these patterns of microdiversity have focused on the abiotic environment, but few have considered how biotic interactions could drive patterns of microbiome diversity. We dissected the patterns, processes, and mechanisms shaping the ecological distributions of three closely related Staphylococcus species in cheese rind biofilms. Paradoxically, the most abundant species (S. equorum) is the slowest colonizer and weakest competitor based on growth and competition assays in the laboratory. Through in vitro community reconstructions, we determined that biotic interactions with neighboring fungi help resolve this paradox. Species-specific stimulation of the poor competitor by fungi of the genus Scopulariopsis allows S. equorum to dominate communities in vitro as it does in situ Results of comparative genomic and transcriptomic experiments indicate that iron utilization pathways, including a homolog of the S. aureus staphyloferrin B siderophore operon pathway, are potential molecular mechanisms underlying Staphylococcus-Scopulariopsis interactions. Our integrated approach demonstrates that fungi can structure the ecological distributions of closely related bacterial species, and the data highlight the importance of bacterium-fungus interactions in attempts to design and manipulate microbiomes.ImportanceDecades of culture-based studies and more recent metagenomic studies have demonstrated that bacterial species in agriculture, medicine, industry, and nature are unevenly distributed across time and space. The ecological processes and molecular mechanisms that shape these distributions are not well understood because it is challenging to connect in situ patterns of diversity with mechanistic in vitro studies in the laboratory. Using tractable cheese rind biofilms and a focus on coagulase-negative staphylococcus (CNS) species, we demonstrate that fungi can mediate the ecological distributions of closely related bacterial species. One of the Staphylococcus species studied, S. saprophyticus, is a common cause of urinary tract infections. By identifying processes that control the abundance of undesirable CNS species, cheese producers will have more precise control on the safety and quality of their products. More generally, Staphylococcus species frequently co-occur with fungi in mammalian microbiomes, and similar bacterium-fungus interactions may structure bacterial diversity in these systems.

Project description:The family Orchidaceae is not only one of the most diverse families of flowering plants, but also one of the most endangered plant taxa. Therefore, understanding how its species richness varies along geographical and environmental gradients is essential for conservation efforts. However, such knowledge is rarely available, especially on a large scale. We used a database extracted from herbarium records to investigate the relationships between orchid species richness and elevation, and to examine how elevational diversity in Yunnan Province, China, might be explained by mid-domain effect (MDE), species-area relationship (SAR), water-energy dynamics (WED), Rapoport's Rule, and climatic variables. This particular location was selected because it is one of the primary centers of distribution for orchids. We recorded 691 species that span 127 genera and account for 88.59% of all confirmed orchid species in Yunnan. Species richness, estimated at 200-m intervals along a slope, was closely correlated with elevation, peaking at 1395 to 1723 m. The elevational pattern of orchid richness was considerably shaped by MDE, SAR, WED, and climate. Among those four predictors, climate was the strongest while MDE was the weakest for predicting the elevational pattern of orchid richness. Species richness showed parabolic responses to mean annual temperature (MAT) and mean annual precipitation (MAP), with maximum richness values recorded at 13.7 to 17.7°C for MAT and 1237 to 1414 mm for MAP. Rapoport's Rule also helped to explain the elevational pattern of species richness in Yunnan, but those influences were not entirely uniform across all methods. These results suggested that the elevational pattern of orchid species richness in Yunnan is collectively shaped by several mechanisms related to geometric constraints, size of the land area, and environments. Because of the dominant role of climate in determining orchid richness, our findings may contribute to a better understanding of the potential effects of climate change on orchid diversity, and the development of conservation strategies for orchids.

Dataset Information

Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients.

Publications

Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets