scholarly journals Fine‐scale observations of spatio‐spectro‐temporal dynamics of bird vocalizations using robot audition techniques

2020 ◽  
Author(s):  
Shinji Sumitani ◽  
Reiji Suzuki ◽  
Shiho Matsubayashi ◽  
Takaya Arita ◽  
Kazuhiro Nakadai ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Fine Scale ◽  
Robot Audition

scholarly journals Fine-scale spatial and temporal dynamics of kdr haplotypes in Aedes aegypti from Mexico

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Marissa K. Grossman ◽  
Julian Rodriguez ◽  
Anuar Medina Barreiro ◽  
Audrey Lenhart ◽  
Pablo Manrique-Saide ◽  
...  
Keyword(s):  
Aedes Aegypti ◽  
Temporal Dynamics ◽  
Fine Scale ◽  
Spatial And Temporal Dynamics

scholarly journals Fine-scale temporal dynamics of herpes virus and vibrios in seawater during a polymicrobial infection in the Pacific oyster Crassostrea gigas

2019 ◽  
Vol 135 (2) ◽  
pp. 97-106 ◽  
Author(s):  
B Petton ◽  
J de Lorgeril ◽  
G Mitta ◽  
G Daigle ◽  
F Pernet ◽  
...  
Keyword(s):  
Crassostrea Gigas ◽  
Herpes Virus ◽  
Pacific Oyster ◽  
Temporal Dynamics ◽  
Polymicrobial Infection ◽  
Fine Scale ◽  
The Pacific ◽  
Herpès Virus

scholarly journals Fine-Scale Temporal Dynamics of a Fragmented Lotic Microbial Ecosystem

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Amitai Or ◽  
Lilach Shtrasler ◽  
Uri Gophna
Keyword(s):  
Temporal Dynamics ◽  
Fine Scale ◽  
Microbial Ecosystem

High frequency monitoring reveals fine scale spatial and temporal dynamics of the deep chlorophyll maximum of a stratified coastal lagoon

2019 ◽  
Vol 218 ◽  
pp. 278-291 ◽  
Author(s):  
Elvira de Eyto ◽  
Sean Kelly ◽  
Elizabeth Ryder ◽  
Mary Dillane ◽  
Lorraine Archer ◽  
...  
Keyword(s):  
Coastal Lagoon ◽  
High Frequency ◽  
Temporal Dynamics ◽  
Deep Chlorophyll Maximum ◽  
Fine Scale ◽  
Chlorophyll Maximum ◽  
Spatial And Temporal Dynamics ◽  
Frequency Monitoring

scholarly journals Fine scale prediction of ecological community composition using a two-step sequential machine learning ensemble

2021 ◽  
Author(s):  
Icíar Civantos ◽  
Javier García-Algarra ◽  
David García-Callejas ◽  
Javier Galeano ◽  
Oscar Godoy ◽  
...  
Keyword(s):  
Species Composition ◽  
Species Interactions ◽  
Predictive Power ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Series Data ◽  
Soil Conditions ◽  
Fine Scale ◽  
Species Abundances

Prediction is one the last frontiers in ecology. Indeed, predicting fine scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models informed by ecological deterministic processes to predict species abundances using reasonably easy to obtain data. To overcome the classical procedure in ecology of parameterizing complex population models of multiple species interactions and poor predictive power, we followed instead a sequential data-driven modeling approach. We use this framework to predict species abundances over 5 years in a highly diverse annual plant community. Our models show a surprisingly high spatial predictive accuracy using only easy to measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggest that predicting the temporal dimension of our system requires longer time series data. Such data would likely capture additional sources of variability that determine temporal patterns of species abundances. In addition, we show that these data-driven models can also inform back mechanistic models of important missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Being able to gain predictive power at fine-scale species composition while maintaining a mechanistic understanding of the underlying processes can be a pivotal tool for conservation, specially given the human induced rapid environmental changes we are experiencing. Here, we document how this objective can be achieved by promoting the interplay between classic modelling approaches in ecology and recently developed data-driven models.

scholarly journals Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

2021 ◽  
Vol 17 (12) ◽  
pp. e1008906
Author(s):  
Icíar Civantos-Gómez ◽  
Javier García-Algarra ◽  
David García-Callejas ◽  
Javier Galeano ◽  
Oscar Godoy ◽  
...  
Keyword(s):  
Species Composition ◽  
Species Interactions ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Predictive Accuracy ◽  
Data Driven ◽  
Soil Conditions ◽  
Fine Scale ◽  
Data Set ◽  
Species Abundances

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.

Fine-scale foraging effort and efficiency of Macaroni penguins is influenced by prey type, patch density and temporal dynamics

2021 ◽  
Vol 168 (1) ◽  
Author(s):  
G. J. Sutton ◽  
C. A. Bost ◽  
A. Z. Kouzani ◽  
S. D. Adams ◽  
K. Mitchell ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Prey Type ◽  
Fine Scale ◽  
Foraging Effort ◽  
Patch Density

scholarly journals Non-Invasive Monitoring of the Spatio-Temporal Dynamics of Vocalizations among Songbirds in a Semi Free-Flight Environment Using Robot Audition Techniques

Birds ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 158-172
Author(s):  
Shinji Sumitani ◽  
Reiji Suzuki ◽  
Takaya Arita ◽  
Kazuhiro Nakadai ◽  
Hiroshi G. Okuno
Keyword(s):  
Social Interactions ◽  
Temporal Dynamics ◽  
Acoustic Properties ◽  
Simple Method ◽  
Non Invasive ◽  
Robot Audition ◽  
Realistic Situation ◽  
The Social ◽  
Spatio Temporal

To understand the social interactions among songbirds, extracting the timing, position, and acoustic properties of their vocalizations is essential. We propose a framework for automatic and fine-scale extraction of spatial-spectral-temporal patterns of bird vocalizations in a densely populated environment. For this purpose, we used robot audition techniques to integrate information (i.e., the timing, direction of arrival, and separated sound of localized sources) from multiple microphone arrays (array of arrays) deployed in an environment, which is non-invasive. As a proof of concept of this framework, we examined the ability of the method to extract active vocalizations of multiple Zebra Finches in an outdoor mesh tent as a realistic situation in which they could fly and vocalize freely. We found that localization results of vocalizations reflected the arrangements of landmark spots in the environment such as nests or perches and some vocalizations were localized at non-landmark positions. We also classified their vocalizations as either songs or calls by using a simple method based on the tempo and length of the separated sounds, as an example of the use of the information obtained from the framework. Our proposed approach has great potential to understand their social interactions and the semantics or functions of their vocalizations considering the spatial relationships, although detailed understanding of the interaction would require analysis of more long-term recordings.

scholarly journals Resolving fine-scale spatio-temporal dynamics in the harbour porpoise Phocoena phocoena

2008 ◽  
Vol 373 ◽  
pp. 173-186 ◽  
Author(s):  
H Skov ◽  
F Thomsen
Keyword(s):  
Temporal Dynamics ◽  
Harbour Porpoise ◽  
Fine Scale ◽  
Phocoena Phocoena ◽  
Spatio Temporal

scholarly journals Fine-Scale Genetic Structure and Reproductive Biology of the Blueberry Pathogen Monilinia vaccinii-corymbosi

2017 ◽  
Vol 107 (2) ◽  
pp. 231-239 ◽  
Author(s):  
Kathleen M. Burchhardt ◽  
Megan E. Miller ◽  
William O. Cline ◽  
Marc A. Cubeta
Keyword(s):  
United States ◽  
Reproductive Biology ◽  
Temporal Dynamics ◽  
The United States ◽  
Spore Production ◽  
Fine Scale ◽  
Genetic Structuring ◽  
Time Points ◽  
Population Structuring ◽  
Broad Spatial Scale

The fungus Monilinia vaccinii-corymbosi, a pathogen of Vaccinium spp., requires asexual and sexual spore production to complete its life cycle. A recent study found population structuring of M. vaccinii-corymbosi over a broad spatial scale in the United States. In this study, we examined fine-scale genetic structuring, temporal dynamics, and reproductive biology within a 125-by-132-m blueberry plot from 2010 to 2012. In total, 395 isolates of M. vaccinii-corymbosi were sampled from infected shoots and fruit to examine their multilocus haplotype (MLH) using microsatellite markers. The MLH of 190 single-ascospore isolates from 21 apothecia was also determined. Little to no genetic differentiation and unrestricted gene flow were detected among four sampled time points and between infected tissue types. Discriminant analysis of principal components suggested genetic structuring within the field, with at least K = 3 genetically distinct clusters maintained over four sampled time points. Single-ascospore progeny from eight apothecia had identical MLH and at least two distinct MLH were detected from 13 apothecia. Tests for linkage disequilibrium suggested that genetically diverse ascospore progeny were the product of recombination. This study supports the idea that the fine-scale dynamics of M. vaccinii-corymbosi may be complex, with genetic structuring, inbreeding, and outcrossing detected in the study area.

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