A simple Bayesian method of inferring extinction : comment

Author Posting. © Ecological Society of America, 2016. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 97 (2016): 796–798, doi:10.1890/15-0336.1

River dolphins can act as population trend indicators in degraded freshwater systems : comment

Author Posting. © Ecological Society of America, 2015. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 96 (2015): 2027-2028, doi:10.1890/14-1900.1

On the attribution of a single event to climate change

Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 27 (2014): 8297–8301, doi:10.1175/JCLI-D-14-00399.1.There is growing interest in assessing the role of climate change in observed extreme weather events. Recent work in this area has focused on estimating a measure called attributable risk. A statistical formulation of this problem is described and used to construct a confidence interval for attributable risk. The resulting confidence is shown to be surprisingly wide even in the case where the event of interest is unprecedented in the historical record.GH acknowledges funding from the Federal Ministry for Education and Research. MA acknowledges partial support from the Giannini Foundation.2015-05-1

Resource allocation for Lagrangian tracking

Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 33 (2016): 1225-1235, doi:10.1175/JTECH-D-15-0115.1.Accurate estimation of the transport probabilities among regions in the ocean provides valuable information for understanding plankton transport, the spread of pollutants, and the movement of water masses. Individual-based particle-tracking models simulate a large ensemble of Lagrangian particles and are a common method to estimate these transport probabilities. Simulating a large ensemble of Lagrangian particles is computationally expensive, and appropriately allocating resources can reduce the cost of this method. Two universal questions in the design of studies that use Lagrangian particle tracking are how many particles to release and how to distribute particle releases. A method is presented for tailoring the number and the release location of particles to most effectively achieve the objectives of a study. The method detailed here is a sequential analysis procedure that seeks to minimize the number of particles that are required to satisfy a predefined metric of result quality. The study assesses the result quality as the precision of the estimates for the elements of a transport matrix and also describes how the method may be extended for use with other metrics. Applying this methodology to both a theoretical system and a particle transport model of the Gulf of Maine results in more precise estimates of the transport probabilities with fewer particles than from uniformly or randomly distributing particle releases. The application of this method can help reduce the cost of and increase the robustness of results from studies that use Lagrangian particles.This research was supported by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) program and the National Science Foundation through Grant OCE-1459133 and Grant OCE-1031256.2016-12-0

Inferring functional extinction based on sighting records

© The Author(s), 2016. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biological Conservation 199 (2016): 84-87, doi:10.1016/j.biocon.2016.04.034.The term functional extinction is used to describe a permanent failure of reproduction or recruitment in a population. Functional extinction results in a truncation of the age distribution, but this can be very difficult to detect in poorly studied populations. Here, we describe a novel statistical method for detecting functional extinction based on a sighting record of individuals of known or estimated ages. The method is based on a simple population dynamics model and simulation results show that it works well even with limited data. The method is illustrated using a sighting record of the ship sturgeon (Acipenser nudiventris) in the Danube River. The results indicate that this population is functionally extinct, most likely by 2002. Management implications of this finding are discussed.The authors also acknowledge the sponsorship provided by the Alexander von Humboldt Foundation and the Federal German Ministry for Education and Research, as well as the support by the Project No. 173045, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia.2017-05-1

On alpha stable distribution of wind driven water surface wave slope

We propose a new formulation of the probability distribution function of wind driven water surface slope with an $\alpha$-stable distribution probability. The mathematical formulation of the probability distribution function is given under an integral formulation. Application to represent the probability of time slope data from laboratory experiments is carried out with satisfactory results. We compare also the $\alpha$-stable model of the water surface slopes with the Gram-Charlier development and the non-Gaussian model of Liu et al\cite{Liu}. Discussions and conclusions are conducted on the basis of the data fit results and the model analysis comparison.Comment: final version of the manuscript: 25 page

Coral reef species assemblages are associated with ambient soundscapes

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Inter-Research for personal use, not for redistribution. The definitive version was published in Marine Ecology Progress Series 533 (2015): 93-107, doi:10.3354/meps11382.Coral reefs provide a wide array of ecosystem services and harbor some of the highest levels of biodiversity on the planet, but many reefs are in decline worldwide. Tracking changes is necessary for effective resource management. Biological sounds have been suggested as a means to quantify ecosystem health and biodiversity, but this requires an understanding of natural bioacoustic variability and relationships to the taxa present. This investigation sought to characterize spatial and temporal variation in biological sound production within and among reefs that varied in their benthic and fish diversity. Multiple acoustic recorders were deployed for intensive 24-hour periods and longer term (~4-month) duty-cycled deployments on three reefs that varied in coral cover and fish density. Short-term results suggest that while there were statistically significant acoustic differences among recorders on a given reef, these differences were relatively small, indicating that a single sensor may be suitable for acoustic characterization of reefs. Analyses of sounds recorded over ~4 months indicated that the strength of diel trends in a low frequency fish band (100-1000 Hz) was correlated with coral cover and fish density but the strength of high-frequency snapping-shrimp (2-20 kHz) trends was not, suggesting that low-frequency recordings may be better indicators of the species assemblages present. Power spectra varied within reefs over the deployment periods, underscoring the need for long-duration recordings to characterize these trends. These findings suggest that, in spite of considerable spatial and temporal variability within reef soundscapes, diel trends in low-frequency sound production correlate with reef species assemblages.This research was funded by the Mitsubishi Corporation Foundation for the Americas and WHOI’s Access to the Sea program

Extreme statistics for time series: Distribution of the maximum relative to the initial value

The extreme statistics of time signals is studied when the maximum is measured from the initial value. In the case of independent, identically distributed (iid) variables, we classify the limiting distribution of the maximum according to the properties of the parent distribution from which the variables are drawn. Then we turn to correlated periodic Gaussian signals with a 1/f^alpha power spectrum and study the distribution of the maximum relative height with respect to the initial height (MRH_I). The exact MRH_I distribution is derived for alpha=0 (iid variables), alpha=2 (random walk), alpha=4 (random acceleration), and alpha=infinity (single sinusoidal mode). For other, intermediate values of alpha, the distribution is determined from simulations. We find that the MRH_I distribution is markedly different from the previously studied distribution of the maximum height relative to the average height for all alpha. The two main distinguishing features of the MRH_I distribution are the much larger weight for small relative heights and the divergence at zero height for alpha>3. We also demonstrate that the boundary conditions affect the shape of the distribution by presenting exact results for some non-periodic boundary conditions. Finally, we show that, for signals arising from time-translationally invariant distributions, the density of near extreme states is the same as the MRH_I distribution. This is used in developing a scaling theory for the threshold singularities of the two distributions.Comment: 29 pages, 4 figure

Species–area relationships always overestimate extinction rates from habitat loss : comment

Author Posting. © Ecological Society of America, 2013. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 94 (2013): 761–763, doi:10.1890/12-0047.1.The species–area relationship summarizes the relationship between the average number of species in a region and its area. This relationship provides a basis for predicting the loss of species associated with loss of habitat (e.g., Pimm and Raven 2000). The approach involves two steps. First, as discussed in more detail below, the species–area relationship is used to predict the number of species that are endemic to the habitat at risk based on its area. Second, these endemic species are assumed to become extinct should this habitat be lost. In a controversial paper, He and Hubbell (2011) argued that the way in which the species–area relationship is used to predict the number of endemic species is incorrect when individual organisms are aggregated in space and argued that this explains a discrepancy between predicted and observed extinction rates associated with habitat loss. The controversy surrounding the paper focused primarily on the second part of their argument (Brooks 2011, Evans et al. 2011, He and Hubbell 2012, Pereira et al. 2012, Thomas and Williamson 2012). Here, we focus on the details underlying the first part.U. Roll is supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. L. Stone is supported by the Israeli Science Foundation

Effects of particle composition on thorium scavenging in the North Atlantic

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Geochimica et Cosmochimica Acta 233 (2018): 115-134, doi:10.1016/j.gca.2018.04.035.The dependence of thorium scavenging by particles on particle composition is examined at selected stations of the U.S. GEOTRACES North Atlantic Section (GA03). Scavenging is here described by the apparent, first-order rate constant of Th adsorption onto particles (k1), as estimated from an inversion of Th radioisotope and radioactive parent data. Our k1 estimates are regressed against particle phase data using two different models. Model I considers biogenic particles (POC+PIC+bSi), lithogenic particles, Mn (oxyhydr)oxides, and Fe (oxyhydr)oxides as regressors, and k1 as the regressand. Model II considers ln(POC+PIC+bSi), ln(lithogenic particles), ln(Mn (oxyhydr)oxides), and ln(Fe (oxyhydr)oxides) as regressors, and ln(k1) as the regressand, where ln() denotes the natural logarithm. Thus, models I and II posit that the effects of particle phases on k1 are, respectively, additive and multiplicative. These models are applied to three groups of stations: (i) all selected stations, (ii) stations west of theMauritanian upwelling region (“western stations”), and (iii) stations within that region (“eastern stations”). We find that model II appears to better describe the effect of particle composition on k1 than model I. Particle composition explains a larger fraction of the variance of k1 for the eastern stations (R2 = 0.60 for model I and 0.67 for model II) than for the western stations (R2 = 0.26 for model I and 0.39 for model II). When considering all stations, the variance of k1 explained by particle composition is intermediate (R2 = 0.50 for model I and 0.51 for model II). According to model II, the variance of k1 explained by particle composition is predominantly due to biogenic particles at the eastern stations and to Mn (oxyhydr)oxides at the western stations. Additionally, we find that particle composition does not explain a significantly different proportion of variance of k1 than particle concentration. It is thus concluded that, at our selected stations, (i) biogenic particles andMn (oxyhydr)oxides more strongly influence Th scavenging than any other phases considered, and (ii) particle composition and particle concentration have comparable effects on this process.We acknowledge the U.S. National Science Foundation for supporting this study (grant OCE-1232578) and the U.S. GEOTRACES North Atlantic section ship time, sampling, and data analysis