scholarly journals Phylogenetic rewiring in mycorrhizal–plant interaction networks increases community stability in naturally fragmented landscapes

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Alicia Montesinos-Navarro ◽  
Gisela Díaz ◽  
Pilar Torres ◽  
Fuensanta Caravaca ◽  
Antonio Roldán
Keyword(s):  
Species Interactions ◽  
Global Changes ◽  
Ecological Interactions ◽  
Community Stability ◽  
Mycorrhizal Plant ◽  
Phylogenetic Constraint ◽  
Plant Interaction ◽  
Mycorrhizal Networks ◽  
Close Relatives ◽  
Preferred Hosts

AbstractAlthough ecological networks are usually considered a static representation of species’ interactions, the interactions can change when the preferred partners are absent (rewiring). In mutualistic networks, rewiring with non-preferred partners can palliate extinction cascades, contributing to communities’ stability. In spite of its significance, whether general patterns can shape the rewiring of ecological interactions remains poorly understood. Here, we show a phylogenetic constraint in the rewiring of mycorrhizal networks, so that rewired interactions (i.e., with non-preferred hosts) tend to involve close relatives of preferred hosts. Despite this constraint, rewiring increases the robustness of the fungal community to the simulated loss of their host species. We identify preferred and non-preferred hosts based on the probability that, when the two partners co-occur, they actually interact. Understanding general patterns in the rewiring of interactions can improve our predictions of community responses to interactions’ loss, which influences how global changes will affect ecosystem stability.

scholarly journals Ecological Interactions and the Netflix Problem

2016 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions ◽  
Learning Techniques

0AbstractSpecies interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

Ecological Interactions and Macroevolution: A New Field with Old Roots

2020 ◽  
Vol 51 (1) ◽  
pp. 215-243 ◽  
Author(s):  
David H. Hembry ◽  
Marjorie G. Weber
Keyword(s):  
Species Interactions ◽  
Interspecific Interactions ◽  
Biotic Interactions ◽  
Evolutionary Change ◽  
Future Research ◽  
Ecological Interactions ◽  
Open Questions ◽  
Work Done ◽  
Lineage Diversification

Linking interspecific interactions (e.g., mutualism, competition, predation, parasitism) to macroevolution (evolutionary change on deep timescales) is a key goal in biology. The role of species interactions in shaping macroevolutionary trajectories has been studied for centuries and remains a cutting-edge topic of current research. However, despite its deep historical roots, classic and current approaches to this topic are highly diverse. Here, we combine historical and contemporary perspectives on the study of ecological interactions in macroevolution, synthesizing ideas across eras to build a zoomed-out picture of the big questions at the nexus of ecology and macroevolution. We discuss the trajectory of this important and challenging field, dividing research into work done before the 1970s, research between 1970 and 2005, and work done since 2005. We argue that in response to long-standing questions in paleobiology, evidence accumulated to date has demonstrated that biotic interactions (including mutualism) can influence lineage diversification and trait evolution over macroevolutionary timescales, and we outline major open questions for future research in the field.

ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME-SERIES DATA

2003 ◽  
Vol 73 (2) ◽  
pp. 301-330 ◽  
Author(s):  
A. R. Ives ◽  
B. Dennis ◽  
K. L. Cottingham ◽  
S. R. Carpenter
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Ecological Interactions ◽  
Community Stability

scholarly journals Sex, Competition and Mimicry : an eco-evolutionary model reveals how ecological interactions shape the evolution of phenotypes in sympatry

2020 ◽  
Author(s):  
Boussens-Dumon Grégoire ◽  
Llaurens Violaine
Keyword(s):  
Species Interactions ◽  
Deterministic Model ◽  
Evolutionary Model ◽  
Sympatric Species ◽  
Reproductive Interference ◽  
Ecological Interactions ◽  
Phenotypic Evolution ◽  
Phenotypic Divergence ◽  
Colour Patterns ◽  
The Impact

1AbstractPhenotypic evolution in sympatric species can be strongly impacted by species interactions, either mutualistic or antagonistic, which may favour local phenotypic divergence or convergence. Interspecific sexual interactions between sympatric species has been shown to favour phenotypic divergence of traits used as sexual cues for example. Those traits may also be involved in local adaptation or in other types of species interactions resulting in complex evolution of traits shared by sympatric species. Here we focus on mimicry and study how reproductive interference may impair phenotypic convergence between species with various levels of defences. We use a deterministic model assuming two sympatric species and where individuals can display two different warning colour patterns. This eco-evolutionary model explores how ecological interactions shape phenotypic evolution within sympatric species. We investigate the effect of (1) the opposing density-dependent selections exerted on colour patterns by predation and reproductive behaviour, and (2) the impact of relative species and phenotype abundances on the fitness costs faced by each individual depending on their species and phenotype. Our model shows that reproductive interference may limit the convergent effect of mimetic interactions and may promote phenotypic divergence between Müllerian mimics. The divergent and convergent evolution of traits also strongly depends on the relative species and phenotype abundances and levels of trophic competition, highlighting how the eco-evolutionary feedbacks between phenotypic evolution and species abundances may result in strikingly different evolutionary routes.

scholarly journals A Holling Functional Response Model for Mapping QTLs Governing Interspecific Interactions

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Huiying Gong ◽  
Qing Fang ◽  
Xuli Zhu ◽  
Libo Jiang ◽  
...  
Keyword(s):  
Functional Response ◽  
Species Interactions ◽  
Community Dynamics ◽  
Interspecific Interactions ◽  
Microbial Interactions ◽  
Ecological Communities ◽  
Response Model ◽  
Ecological Interactions ◽  
Dynamic Complexity ◽  
Genome Level

Genes play an important role in community ecology and evolution, but how to identify the genes that affect community dynamics at the whole genome level is very challenging. Here, we develop a Holling type II functional response model for mapping quantitative trait loci (QTLs) that govern interspecific interactions. The model, integrated with generalized Lotka-Volterra differential dynamic equations, shows a better capacity to reveal the dynamic complexity of inter-species interactions than classic competition models. By applying the new model to a published mapping data from a competition experiment of two microbial species, we identify a set of previously uncharacterized QTLs that are specifically responsible for microbial cooperation and competition. The model can not only characterize how these QTLs affect microbial interactions, but also address how change in ecological interactions activates the genetic effects of the QTLs. This model provides a quantitative means of predicting the genetic architecture that shapes the dynamic behavior of ecological communities.

scholarly journals Differences in insectivore bird diets in coffee agroecosystems driven by obligate or generalist guild, shade management, season, and year

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12296
Author(s):  
Julie A. Jedlicka ◽  
Stacy M. Philpott ◽  
Martha L. Baena ◽  
Peter Bichier ◽  
Thomas V. Dietsch ◽  
...  
Keyword(s):  
Resource Competition ◽  
Migratory Birds ◽  
Stomach Contents ◽  
Management Systems ◽  
Ecological Interactions ◽  
Community Stability ◽  
Insectivorous Birds ◽  
Catharus Ustulatus ◽  
Avian Biodiversity ◽  
Foraging Guilds

Neotropical shade-grown coffee systems are renowned for their potential to conserve avian biodiversity. Yet, little is known about food resources consumed by insectivorous birds in these systems, the extent of resource competition between resident and migratory birds, or how management of shade trees might influence diet selection. We identified arthropods in stomach contents from obligate and generalist insectivorous birds captured in mist-nets at five coffee farms in Chiapas, Mexico between 2001–2003. Overall stomach contents from 938 individuals revealed dietary differences resulting from changes in seasons, years, and foraging guilds. Of four species sampled across all management systems, Yellow-green Vireo (Vireo flavoviridis) prey differed depending on coffee shade management, consuming more ants in shaded monoculture than polyculture systems. Diets of obligate and generalist resident insectivores were 72% dissimilar with obligate insectivores consuming more Coleoptera and Araneae, and generalist insectivores consuming more Formicidae and other Hymenoptera. This suggests that obligate insectivores target more specialized prey whereas generalist insectivores rely on less favorable, chemically-defended prey found in clumped distributions. Our dataset provides important natural history data for many Nearctic-Neotropical migrants such as Tennessee Warbler (Leiothlypis peregrina; N = 163), Nashville Warbler (Leiothlypis ruficapilla; N = 69), and Swainson’s Thrush (Catharus ustulatus; N = 68) and tropical residents including Red-legged Honeycreepers (Cyanerpes cyaneus; N = 70) and Rufous-capped Warblers (Basileuterus rufifrons; N = 56). With declining arthropod populations worldwide, understanding the ecological interactions between obligate and generalist avian insectivores gives researchers the tools to evaluate community stability and inform conservation efforts.

scholarly journals Ecological interactions and the Netflix problem

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3644 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions

Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

scholarly journals Evolutionary relatedness does not predict competition and co-occurrence in natural or experimental communities of green algae

2015 ◽  
Vol 282 (1799) ◽  
pp. 20141745 ◽  
Author(s):  
Markos A. Alexandrou ◽  
Bradley J. Cardinale ◽  
John D. Hall ◽  
Charles F. Delwiche ◽  
Keith Fritschie ◽  
...  
Keyword(s):  
Green Algae ◽  
Species Interactions ◽  
Laboratory Experiments ◽  
Evolutionary Relationships ◽  
Biological Traits ◽  
Phylogenetic Distance ◽  
Ecological Data ◽  
Experimental Systems ◽  
Evolutionary Relatedness ◽  
Close Relatives

The competition-relatedness hypothesis (CRH) predicts that the strength of competition is the strongest among closely related species and decreases as species become less related. This hypothesis is based on the assumption that common ancestry causes close relatives to share biological traits that lead to greater ecological similarity. Although intuitively appealing, the extent to which phylogeny can predict competition and co-occurrence among species has only recently been rigorously tested, with mixed results. When studies have failed to support the CRH, critics have pointed out at least three limitations: (i) the use of data poor phylogenies that provide inaccurate estimates of species relatedness, (ii) the use of inappropriate statistical models that fail to detect relationships between relatedness and species interactions amidst nonlinearities and heteroskedastic variances, and (iii) overly simplified laboratory conditions that fail to allow eco-evolutionary relationships to emerge. Here, we address these limitations and find they do not explain why evolutionary relatedness fails to predict the strength of species interactions or probabilities of coexistence among freshwater green algae. First, we construct a new data-rich, transcriptome-based phylogeny of common freshwater green algae that are commonly cultured and used for laboratory experiments. Using this new phylogeny, we re-analyse ecological data from three previously published laboratory experiments. After accounting for the possibility of nonlinearities and heterogeneity of variances across levels of relatedness, we find no relationship between phylogenetic distance and ecological traits. In addition, we show that communities of North American green algae are randomly composed with respect to their evolutionary relationships in 99% of 1077 lakes spanning the continental United States. Together, these analyses result in one of the most comprehensive case studies of how evolutionary history influences species interactions and community assembly in both natural and experimental systems. Our results challenge the generality of the CRH and suggest it may be time to re-evaluate the validity and assumptions of this hypothesis.

scholarly journals Processes entangling interactions in communities: forbidden links are more important than abundance in a hummingbird–plant network

2014 ◽  
Vol 281 (1780) ◽  
pp. 20132397 ◽  
Author(s):  
Jeferson Vizentin-Bugoni ◽  
Pietro Kiyoshi Maruyama ◽  
Marlies Sazima
Keyword(s):  
Network Structure ◽  
Species Interactions ◽  
Species Abundance ◽  
Interaction Network ◽  
Atlantic Rainforest ◽  
Relative Importance ◽  
Ecological Network ◽  
Plant Interaction ◽  
Multiple Processes ◽  
Network Metrics

Understanding the relative importance of multiple processes on structuring species interactions within communities is one of the major challenges in ecology. Here, we evaluated the relative importance of species abundance and forbidden links in structuring a hummingbird–plant interaction network from the Atlantic rainforest in Brazil. Our results show that models incorporating phenological overlapping and morphological matches were more accurate in predicting the observed interactions than models considering species abundance. This means that forbidden links, by imposing constraints on species interactions, play a greater role than species abundance in structuring the ecological network. We also show that using the frequency of interaction as a proxy for species abundance and network metrics to describe the detailed network structure might lead to biased conclusions regarding mechanisms generating network structure. Together, our findings suggest that species abundance can be a less important driver of species interactions in communities than previously thought.

scholarly journals How molecular tools can help understanding species interactions to improve restoration outcomes

2016 ◽  
Author(s):  
Stéphane Boyer ◽  
Benjamin R Waterhouse ◽  
Steve D Wratten
Keyword(s):  
Species Interactions ◽  
Restoration Ecology ◽  
Environmental Dna ◽  
Ecological Interactions ◽  
Molecular Tools ◽  
Predator Prey ◽  
Prey Animal ◽  
Restoration Practice ◽  
High Throughput Dna Sequencing ◽  
The Cost

Preserving species interactions should be a key desired outcome in restoration ecology. With progress in environmental DNA techniques and the dramatic reduction in the cost of high-throughput DNA sequencing, large amounts of information can be gathered on how species interact with little to no disturbance to ecosystems. Here, we argue that the use of molecular tools to study ecological interactions will become increasingly important in restoration projects. We describe specific examples where recent advances in genetics allow for a better understanding of predator-prey, animal-plant, plant-microbe and trophic cascade interactions, which can inform restoration practice and substantially improve our capacity to restore functioning ecosystems.

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