Shopping for Ecological Indices? On the Use of Incidence-Based Species Compositional Similarity Measures

β-diversity has been under continuous debate, with a current need to better understand the way in which a new wave of measures work. We assessed the results of 12 incidence-based β-diversity indices. Our results of gradual species composition overlap between paired assemblages considering progressive differences in species richness show the following: (i) four indices (β-2, β-3, β-3.s, and βr) should be used cautiously given that results with no shared species retrieve results that could be misinterpreted; (ii) all measures conceived specifically as partitioned components of species compositional dissimilarities ought to be used as such and not as independent measures per se; (iii) the non-linear response of some indices to gradual species composition overlap should be interpreted carefully, and further analysis using their results as dependent variables should be performed cautiously; and (iv) two metrics (βsim and βsor) behave predictably and linearly to gradual species composition overlap. We encourage ecologists using measures of β-diversity to fully understand their mathematical nature and type of results under the scenario to be used in order to avoid inappropriate and misleading inferences

Shopping for Ecological Indices? On the Use of Incidence-Based Species Compositional Similarity Measures

β-diversity has been under continuous debate, with a current need to better understand the way in which a new wave of measures work. We assessed the results of 12 incidence-based β-diversity indices. Our results of gradual species composition overlap between paired assemblages considering progressive differences in species richness show the following: (i) four indices (β-2, β-3, β-3.s, and βr) should be used cautiously given that results with no shared species retrieve results that could be misinterpreted; (ii) all measures conceived specifically as partitioned components of species compositional dissimilarities ought to be used as such and not as independent measures per se; (iii) the non-linear response of some indices to gradual species composition overlap should be interpreted carefully, and further analysis using their results as dependent variables should be performed cautiously; and (iv) two metrics (βsim and βsor) behave predictably and linearly to gradual species composition overlap. We encourage ecologists using measures of β-diversity to fully understand their mathematical nature and type of results under the scenario to be used in order to avoid inappropriate and misleading inferences

Inductores de agallas en árboles del género Inga (Fabaceae)

Tesis (maestría académica en biología)--Universidad de Costa Rica. Sistema de Estudios de Posgrado, 2011UCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ciencias Básicas::Maestría Académica en Biologí

L'officinal et le patient arthrosique

CLERMONT FD-BCIU-Santé (631132104) / SudocLYON1-BU Santé (693882101) / SudocSudocFranceF

Driving the blue fleet: Temporal variability and drivers behind bluebottle (Physalia physalis) beachings off Sydney, Australia

Physalia physalis , the bluebottle in Australia, are colonial siphonophores that live at the surface of the ocean, mainly in tropical and subtropical waters. P. physalis are sometimes present in large swarms, and with tentacles capable of intense stings, they can negatively impact public health and commercial fisheries. P. physalis , which does not swim, is advected by ocean currents and winds acting on its gas-filled sail. While previous studies have attempted to model the drift of P. physalis , little is known about its sources, distribution, and the timing of its arrival to shore. In this study, we present a dataset with four years of daily P. physalis beachings and stings reports at three locations off Sydney’s coast in Australia. We investigate the spatial and temporal variability of P. physalis presence (beachings and stings) in relation to different environmental parameters. This dataset shows a clear seasonal pattern where more P. physalis beachings occur in the Austral summer and less in winter. Cold ocean temperatures do not hinder the presence of P. physalis and the temperature seasonal cycle and that observed in P. physalis presence/absence time-series are out of phase by 3-4 months. We identify wind direction as the major driver of the temporal variability of P. physalis arrival to the shore, both at daily and seasonal time-scales. The differences observed between sites of the occurrence of beaching events is consistent with the geomorphology of the coastline which influences the frequency and direction of favorable wind conditions. We also show that rip currents, a physical mechanism occurring at the scale of the beach, can be a predictor of beaching events. This study is a first step towards understanding the dynamics of P. physalis transport and ultimately being able to predict its arrival to the coast and mitigating the number of people who experience painful stings and require medical help

Driving the blue fleet: Temporal variability and drivers behind bluebottle (Physalia physalis) beachings off Sydney, Australia

Physalia physalis , the bluebottle in Australia, are colonial siphonophores that live at the surface of the ocean, mainly in tropical and subtropical waters. P. physalis are sometimes present in large swarms, and with tentacles capable of intense stings, they can negatively impact public health and commercial fisheries. P. physalis , which does not swim, is advected by ocean currents and winds acting on its gas-filled sail. While previous studies have attempted to model the drift of P. physalis , little is known about its sources, distribution, and the timing of its arrival to shore. In this study, we present a dataset with four years of daily P. physalis beachings and stings reports at three locations off Sydney’s coast in Australia. We investigate the spatial and temporal variability of P. physalis presence (beachings and stings) in relation to different environmental parameters. This dataset shows a clear seasonal pattern where more P. physalis beachings occur in the Austral summer and less in winter. Cold ocean temperatures do not hinder the presence of P. physalis and the temperature seasonal cycle and that observed in P. physalis presence/absence time-series are out of phase by 3-4 months. We identify wind direction as the major driver of the temporal variability of P. physalis arrival to the shore, both at daily and seasonal time-scales. The differences observed between sites of the occurrence of beaching events is consistent with the geomorphology of the coastline which influences the frequency and direction of favorable wind conditions. We also show that rip currents, a physical mechanism occurring at the scale of the beach, can be a predictor of beaching events. This study is a first step towards understanding the dynamics of P. physalis transport and ultimately being able to predict its arrival to the coast and mitigating the number of people who experience painful stings and require medical help

Shopping for Ecological Indices? On the Use of Incidence-Based Species Compositional Similarity Measures

β-diversity has been under continuous debate, with a current need to better understand the way in which a new wave of measures work. We assessed the results of 12 incidence-based β-diversity indices. Our results of gradual species composition overlap between paired assemblages considering progressive differences in species richness show the following: (i) four indices (β-2, β-3, β-3.s, and βr) should be used cautiously given that results with no shared species retrieve results that could be misinterpreted; (ii) all measures conceived specifically as partitioned components of species compositional dissimilarities ought to be used as such and not as independent measures per se; (iii) the non-linear response of some indices to gradual species composition overlap should be interpreted carefully, and further analysis using their results as dependent variables should be performed cautiously; and (iv) two metrics (βsim and βsor) behave predictably and linearly to gradual species composition overlap. We encourage ecologists using measures of β-diversity to fully understand their mathematical nature and type of results under the scenario to be used in order to avoid inappropriate and misleading inferences