scholarly journals Random forests to evaluate biotic interactions in fish distribution models

2015 ◽  
Vol 67 ◽  
pp. 173-183 ◽  
Author(s):  
P. Vezza ◽  
R. Muñoz-Mas ◽  
F. Martinez-Capel ◽  
A. Mouton
Keyword(s):  
Random Forests ◽  
Biotic Interactions ◽  
Fish Distribution ◽  
Distribution Models

scholarly journals Accounting for biotic interactions through alpha‐diversity constraints in stacked species distribution models

2017 ◽  
Vol 8 (9) ◽  
pp. 1092-1102 ◽  
Author(s):  
Yoni Gavish ◽  
Charles J. Marsh ◽  
Mathias Kuemmerlen ◽  
Stefan Stoll ◽  
Peter Haase ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Alpha Diversity ◽  
Distribution Models

scholarly journals A framework to bridge scales in distribution modeling of soil microbiota

2020 ◽  
Vol 96 (5) ◽  
Author(s):  
Jonas J Lembrechts ◽  
L Broeders ◽  
J De Gruyter ◽  
D Radujković ◽  
I Ramirez-Rojas ◽  
...  
Keyword(s):  
Environmental Gradients ◽  
Spatial Scales ◽  
Biotic Interactions ◽  
Environmental Niche ◽  
Distribution Models ◽  
Soil Microbiota ◽  
Soil Microbial ◽  
Critical Issues ◽  
Multiple Species ◽  
Species Specific

ABSTRACT Creating accurate habitat suitability and distribution models (HSDMs) for soil microbiota is far more challenging than for aboveground organism groups. In this perspective paper, we propose a conceptual framework that addresses several of the critical issues holding back further applications. Most importantly, we tackle the mismatch between the broadscale, long-term averages of environmental variables traditionally used, and the environment as experienced by soil microbiota themselves. We suggest using nested sampling designs across environmental gradients and objectively integrating spatially hierarchic heterogeneity as covariates in HSDMs. Second, to incorporate the crucial role of taxa co-occurrence as driver of soil microbial distributions, we promote the use of joint species distribution models, a class of models that jointly analyze multiple species’ distributions, quantifying both species-specific environmental responses (i.e. the environmental niche) and covariance among species (i.e. biotic interactions). Our approach allows incorporating the environmental niche and its associated distribution across multiple spatial scales. The proposed framework facilitates the inclusion of the true relationships between soil organisms and their abiotic and biotic environments in distribution models, which is crucial to improve predictions of soil microbial redistributions as a result of global change.

scholarly journals Evidence for large-scale effects of competition: niche displacement in Canada lynx and bobcat

2013 ◽  
Vol 280 (1773) ◽  
pp. 20132495 ◽  
Author(s):  
Michael J. L. Peers ◽  
Daniel H. Thornton ◽  
Dennis L. Murray
Keyword(s):  
Large Scale ◽  
Scale Effects ◽  
Spatial Scales ◽  
Biotic Interactions ◽  
Character Displacement ◽  
Lynx Rufus ◽  
Ecological Communities ◽  
Lynx Canadensis ◽  
Distribution Models ◽  
Canada Lynx

Determining the patterns, causes and consequences of character displacement is central to our understanding of competition in ecological communities. However, the majority of competition research has occurred over small spatial extents or focused on fine-scale differences in morphology or behaviour. The effects of competition on broad-scale distribution and niche characteristics of species remain poorly understood but critically important. Using range-wide species distribution models, we evaluated whether Canada lynx ( Lynx canadensis ) or bobcat ( Lynx rufus ) were displaced in regions of sympatry. Consistent with our prediction, we found that lynx niches were less similar to those of bobcat in areas of sympatry versus allopatry, with a stronger reliance on snow cover driving lynx niche divergence in the sympatric zone. By contrast, bobcat increased niche breadth in zones of sympatry, and bobcat niches were equally similar to those of lynx in zones of sympatry and allopatry. These findings suggest that competitively disadvantaged species avoid competition at large scales by restricting their niche to highly suitable conditions, while superior competitors expand the diversity of environments used. Our results indicate that competition can manifest within climatic niche space across species’ ranges, highlighting the importance of biotic interactions occurring at large spatial scales on niche dynamics.

scholarly journals Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants

Ecography ◽  
2012 ◽  
Vol 36 (6) ◽  
pp. 649-656 ◽  
Author(s):  
Tereza Cristina Giannini ◽  
Daniel S. Chapman ◽  
Antonio Mauro Saraiva ◽  
Isabel Alves-dos-Santos ◽  
Jacobus C. Biesmeijer
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Distribution Models

scholarly journals Co-occurrence variation explains the low host dependence of ambrosia beetles along altitude gradients in SW China

2021 ◽  
Author(s):  
Fang Luo ◽  
LINGZENG MENG ◽  
Jian Wang ◽  
Yan-Hong Liu
Keyword(s):  
Temporal Dynamics ◽  
Hierarchical Modeling ◽  
Species Turnover ◽  
Regional Scale ◽  
Biotic Interactions ◽  
Ambrosia Beetle ◽  
Minor Role ◽  
Climatic Gradient ◽  
Distribution Models ◽  
Ambrosia Beetles

Abstract Background Separation of biotic and abiotic impacts on species diversity distribution patterns across a significant climatic gradient is a challenge in the study of diversity maintenance mechanisms. The basic task is to reconcile scale-dependent effects of abiotic and biotic processes on species distribution models. However, Eltonian noise hypothesis predicted that the effects of biotic interactions will be averaged out at macroscales, and there are many empirical observations that biotic interactions would constrain species distributions at micro-ecological scales. Here, we used a hierarchical modeling method to detect the host specificities of ambrosia beetles (Scolytinae and Platypodinae) with their dependent tree communities across a steep climatic gradient, which was embedded within a relatively homogenous spatial niche. Results Species turnover of both trees and ambrosia beetles have a relatively similar pattern, characterized by the climatic proxy at a regional scale, but not at local scales. This pattern confirmed the Eltonian noise hypothesis wherein emphasis was on influences of macro-climate on local biotic interactions between trees and hosted ambrosia beetle communities, whereas local biotic relations, represented by host specificity dependence, were regionally conserved. Conclusions At a confined spatial scale, cross-taxa comparisons of co-occurrence highlighted the importance of the organism’s dispersal. The effects of tree abundance and phylogeny diversity on ambrosia beetle diversity were, to a large extent, indirect, operating via changes in ambrosia beetle abundance through spatial and temporal dynamics of resources distribution. Tree host dependence plays a minor role on the hosted ambrosia beetle community in this concealed wood decomposing interacting system.

Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA

2013 ◽  
Vol 22 (2) ◽  
pp. 174 ◽  
Author(s):  
Avi Bar Massada ◽  
Alexandra D. Syphard ◽  
Susan I. Stewart ◽  
Volker C. Radeloff
Keyword(s):  
Random Forests ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Distribution Model ◽  
National Forest ◽  
Similar Species ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Manistee National Forest

Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar species-distribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron–Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions.

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