Search

In meteorology, permutation tests are a commonly recommended tool because standard paramet-ric methods of inference often is insufficient when analysing weather. Therefore other methodswith less stringent assumptions like permutation testing is used. Time series data of weathercontains both temporal and spatial autocorrelation that may violate the exchangeability assump-tion of the permutation test. This paper explores the effect of autocorrelation in data on theapproximated permutation test. From a literary study it is found that the type I error of the test,i.e. rejecting the test while the null is true, increases when the assumption of exchangeability isviolated. A simulation study was then preformed on three meteorological variables, temperature,wind and precipitation in Iberia following a cold spell in the US. This increase in the type I er-ror was not found to be significant in the simulated data with autocorrelation ranging from 0 to 0.9.

In meteorology, permutation tests are a commonly recommended tool because standard paramet-ric methods of inference often is insufficient when analysing weather. Therefore other methodswith less stringent assumptions like permutation testing is used. Time series data of weathercontains both temporal and spatial autocorrelation that may violate the exchangeability assump-tion of the permutation test. This paper explores the effect of autocorrelation in data on theapproximated permutation test. From a literary study it is found that the type I error of the test,i.e. rejecting the test while the null is true, increases when the assumption of exchangeability isviolated. A simulation study was then preformed on three meteorological variables, temperature,wind and precipitation in Iberia following a cold spell in the US. This increase in the type I er-ror was not found to be significant in the simulated data with autocorrelation ranging from 0 to 0.9.

© 2020 Journal Compilation Cetaceans are iconic predators that serve as important indicators of marine ecosystem health. The Bremer Sub-Basin, south-western Australia, supports a diverse cetacean community including the largest documented aggregation of killer whales (Orcinus orca) in Australian waters. Knowledge of cetacean distributions is critical for managing the area's thriving ecotourism industry, yet is largely sporadic. Here we combined aerial with opportunistic ship-borne surveys during 2015-2017 to describe the occurrence of multiple cetacean species on a regional scale. We used generalised estimating equations to model variation in killer whale relative density as a function of both static and dynamic covariates, including seabed depth, slope, and chlorophyll a concentration, while accounting for autocorrelation. Encountered cetacean groups included: killer (n = 177), sperm (n = 69), long-finned pilot (n = 29), false killer (n = 2), and strap-toothed beaked (n = 1) whales, as well as bottlenose (n = 12) and common (n = 5) dolphins. Killer whale numbers peaked in areas of low temperatures and high primary productivity, likely due to seasonal upwelling of nutrient-rich waters supporting high prey biomass. The best predictive model highlighted potential killer whale 'hotspots' in the Henry, Hood, Pallinup and Bremer Canyons. This study demonstrates the value of abundance data from platforms of opportunity for marine planning and wildlife management in the open ocean.

© 2020 Journal Compilation Cetaceans are iconic predators that serve as important indicators of marine ecosystem health. The Bremer Sub-Basin, south-western Australia, supports a diverse cetacean community including the largest documented aggregation of killer whales (Orcinus orca) in Australian waters. Knowledge of cetacean distributions is critical for managing the area's thriving ecotourism industry, yet is largely sporadic. Here we combined aerial with opportunistic ship-borne surveys during 2015-2017 to describe the occurrence of multiple cetacean species on a regional scale. We used generalised estimating equations to model variation in killer whale relative density as a function of both static and dynamic covariates, including seabed depth, slope, and chlorophyll a concentration, while accounting for autocorrelation. Encountered cetacean groups included: killer (n = 177), sperm (n = 69), long-finned pilot (n = 29), false killer (n = 2), and strap-toothed beaked (n = 1) whales, as well as bottlenose (n = 12) and common (n = 5) dolphins. Killer whale numbers peaked in areas of low temperatures and high primary productivity, likely due to seasonal upwelling of nutrient-rich waters supporting high prey biomass. The best predictive model highlighted potential killer whale 'hotspots' in the Henry, Hood, Pallinup and Bremer Canyons. This study demonstrates the value of abundance data from platforms of opportunity for marine planning and wildlife management in the open ocean.

Cetaceans are iconic predators that serve as important indicators of marine ecosystem health. The Bremer Sub-Basin, south-western Australia, supports a diverse cetacean community including the largest documented aggregation of killer whales (Orcinus orca) in Australian waters. Knowledge of cetacean distributions is critical for managing the area’s thriving ecotourism industry, yet is largely sporadic. Here we combined aerial with opportunistic ship-borne surveys during 2015–2017 to describe the occurrence of multiple cetacean species on a regional scale. We used generalised estimating equations to model variation in killer whale relative density as a function of both static and dynamic covariates, including seabed depth, slope, and chlorophyll a concentration, while accounting for autocorrelation. Encountered cetacean groups included: killer (n ¼ 177), sperm (n ¼ 69), long-finned pilot (n ¼ 29), false killer (n ¼ 2), and straptoothed beaked (n ¼ 1) whales, as well as bottlenose (n ¼ 12) and common (n ¼ 5) dolphins. Killer whale numbers peaked in areas of low temperatures and high primary productivity, likely due to seasonal upwelling of nutrient-rich waters supporting high prey biomass. The best predictive model highlighted potential killer whale ‘hotspots’ in the Henry, Hood, Pallinup and Bremer Canyons. This study demonstrates the value of abundance data from platforms of opportunity for marine planning and wildlife management in the open ocean.

One of the largest causes of fluctuations in the size and structure of populations is changes in the environment. In nature, these changes are often temporally autocorrelated and the strength and direction of the autocorrelation can affect population dynamics. These effects are mediated by complex, simultaneously occurring ecological and evolutionary processes, such as phenotypic plasticity and selection. Determining how these processes interact to affect responses of different life histories to autocorrelated environmental fluctuations is of paramount importance to infer which taxa are likely to go extinct or become invasive under global change. Here, we assessed the effect of autocorrelation in environmental states on the trait and population dynamics of different life histories using an evolutionary explicit individual-based modeling approach. We found that, in general, higher positive temporal autocorrelation caused more variation in population size. Fast life histories were more affected than slow ones as they were able to adapt more quickly to varying environmental optima and therefore experienced larger initial decreases in population size when optima changed. Including adaptive phenotypic plasticity buffered the effects of autocorrelation on population dynamics while nonadaptive plasticity amplified them, especially for slow life histories, which recovered less from maladaptation. This study highlights that integration of phenotypic plasticity, selection, and population dynamics is important to improve our understanding of how different life histories deal with autocorrelated climate variability.

When collecting panel data, we need to acknowledge that responses do not represent independent measurements. The known apparatus in transportation research offers several opportunities to estimate panel effects for well-known and widely applied models such as hazard and dynamic logit models. However, the transportation research community is not endowed with a rich set of methods to account for panel effects in dynamic probabilistic decision trees, which have been used as a formalism for the representation of decision heuristics. Building on scarce prior work in statistics, we elaborate an approach to estimate panel effects in dynamic probabilistic decision trees with multinomial action states. Given that panel data naturally have a hierarchical structure with repeated measures nested within individuals, we implement a mixed-effects model that simultaneously accounts for population-level effects (fixed effects), between-individual variances (random effects), and within-individual variances (autocorrelations). The approach uses an iterative estimation procedure between CHAID-based probabilistic tree induction and Bayesian generalized linear mixture modeling (GLMM). When extracting the dynamic probabilistic decision trees, it is assumed that the random effects are known, while it is assumed that the fixed effects are known when estimating the Bayesian GLMM. This iterative process continues until convergence is reached. A Monte Carlo technique is used to navigate between aggregate choice probabilities and individual level multinomial choices. We also test the significance of temporal autocorrelation within individuals. The suggested approach is illustrated using charging station choice of users of Plug-in Electric Vehicles (PEV). Results support the potential value of the suggested approach.

When collecting panel data, we need to acknowledge that responses do not represent independent measurements. The known apparatus in transportation research offers several opportunities to estimate panel effects for well-known and widely applied models such as hazard and dynamic logit models. However, the transportation research community is not endowed with a rich set of methods to account for panel effects in dynamic probabilistic decision trees, which have been used as a formalism for the representation of decision heuristics. Building on scarce prior work in statistics, we elaborate an approach to estimate panel effects in dynamic probabilistic decision trees with multinomial action states. Given that panel data naturally have a hierarchical structure with repeated measures nested within individuals, we implement a mixed-effects model that simultaneously accounts for population-level effects (fixed effects), between-individual variances (random effects), and within-individual variances (autocorrelations). The approach uses an iterative estimation procedure between CHAID-based probabilistic tree induction and Bayesian generalized linear mixture modeling (GLMM). When extracting the dynamic probabilistic decision trees, it is assumed that the random effects are known, while it is assumed that the fixed effects are known when estimating the Bayesian GLMM. This iterative process continues until convergence is reached. A Monte Carlo technique is used to navigate between aggregate choice probabilities and individual level multinomial choices. We also test the significance of temporal autocorrelation within individuals. The suggested approach is illustrated using charging station choice of users of Plug-in Electric Vehicles (PEV). Results support the potential value of the suggested approach.

Transgenerational environmental effects can trigger strong phenotypic variation. However, it is unclear how cues from different preceding generations interact. Also, little is known about the genetic variation for these life history traits.Here, we present the effects of grandparental and parental mild heat, and their combination, on four traits of the third‐generation phenotype of 14 Arabidopsis thaliana genotypes. We tested for correlations of these effects with climate and constructed a conceptual model to identify the environmental conditions that favour the parental effect on flowering time. We observed strong evidence for genotype‐specific transgenerational effects. On average, A. thaliana accustomed to mild heat produced more seeds after two generations. Parental effects overruled grandparental effects in all traits except reproductive biomass. Flowering was generally accelerated by all transgenerational effects. Notably, the parental effect triggered earliest flowering in genotypes adapted to dry summers. Accordingly, this parental effect was favoured in the model when early summer heat terminated the growing season and environments were correlated across generations.Our results suggest that A. thaliana can partly accustom to mild heat over two generations and genotype‐specific parental effects show non‐random evolutionary divergence across populations that may support climate change adaptation in the Mediterranean.

From moment to moment, we perceive objects in the world as continuous despite fluctuations in their image properties due to factors like occlusion, visual noise, and eye movements. The mechanism by which the visual system accomplishes this object continuity remains elusive. Recent results demonstrate that the perception of low-level stimulus features such as orientation and numerosity is systematically biased (i.e. pulled) towards visual input from the recent past. The spatial region over which current orientations are pulled by previous orientations is known as the continuity field, which is temporally tuned for the past 10-15 seconds. This perceptual pull could contribute to the visual stability of low-level features over short time periods, but it does not address how visual stability occurs at the level of complex objects or during visual discontinuities. Here, we examined whether the visual system facilitates stable perception by biasing current perception of objects towards recently seen objects, and whether it operates across disruptions in visibility. First, we used psychophysics to show that the perception of face identity is systematically biased towards identities seen up to several seconds prior, even across changes in viewpoint. This perceptual bias did not depend on subjects’ prior responses or on the method used to measure identity perception. Next, we tested whether this serial dependence, or positive perceptual pull, helps stabilize perceived expression. To test this, observers judged random facial expressions (ranging from happy to sad to angry). We found a pull in perceived expression toward previously seen expressions, but only when the 1-back and current face shared the same identity. Finally, we examined whether the continuity field helps maintain perceived object identity during occlusion. Specifically, we found that the perception of an oriented Gabor that emerged from behind an occluder was significantly pulled towards the random (and unrela

From moment to moment, we perceive objects in the world as continuous despite fluctuations in their image properties due to factors like occlusion, visual noise, and eye movements. The mechanism by which the visual system accomplishes this object continuity remains elusive. Recent results demonstrate that the perception of low-level stimulus features such as orientation and numerosity is systematically biased (i.e. pulled) towards visual input from the recent past. The spatial region over which current orientations are pulled by previous orientations is known as the continuity field, which is temporally tuned for the past 10-15 seconds. This perceptual pull could contribute to the visual stability of low-level features over short time periods, but it does not address how visual stability occurs at the level of complex objects or during visual discontinuities. Here, we examined whether the visual system facilitates stable perception by biasing current perception of objects towards recently seen objects, and whether it operates across disruptions in visibility. First, we used psychophysics to show that the perception of face identity is systematically biased towards identities seen up to several seconds prior, even across changes in viewpoint. This perceptual bias did not depend on subjects’ prior responses or on the method used to measure identity perception. Next, we tested whether this serial dependence, or positive perceptual pull, helps stabilize perceived expression. To test this, observers judged random facial expressions (ranging from happy to sad to angry). We found a pull in perceived expression toward previously seen expressions, but only when the 1-back and current face shared the same identity. Finally, we examined whether the continuity field helps maintain perceived object identity during occlusion. Specifically, we found that the perception of an oriented Gabor that emerged from behind an occluder was significantly pulled towards the random (and unrela

The probability of, and time to, fixation of a mutation in a population has traditionally been studied by the classic Wright-Fisher model where population size is constant. Recent theoretical expansions have covered fluctuating populations in various ways but have not incorporated models of how the environment fluctuates in combination with different levels of density-compensation affecting fecundity. We tested the hypothesis that the probability of, and time to, fixation of neutral, advantageous and deleterious mutations is dependent on how the environment fluctuates over time, and on the level of density-compensation. We found that fixation probabilities and times were dependent on the pattern of autocorrelation of carrying capacity over time and interacted with density-compensation. The pattern found was most pronounced at small population sizes. The patterns differed greatly depending on whether the mutation was neutral, advantageous, or disadvantageous. The results indicate that the degree of mismatch between carrying capacity and population size is a key factor, rather than population size per se, and that effective population sizes can be very low also when the census population size is far above the carrying capacity. This study highlights the need for explicit population dynamic models and models for environmental fluctuations for the understanding of the dynamics of genes in populations.

Explaining variation in population growth rates is fundamental to predicting population dynamics and population responses to environmental change. In this study, we used matrix population models, which link birth, growth and survival to population growth rate, to examine how and why population growth rates vary within and among 50 terrestrial plant species. Population growth rates were more similar within species than among species; with phylogeny having a minimal influence on among-species variation. Most population growth rates decreased over the observation period and were negatively autocorrelated between years; that is, higher than average population growth rates tended to be followed by lower than average population growth rates. Population growth rates varied more through time than space; this temporal variation was due mostly to variation in post-seedling survival and for a subset of species was partly explained by response to environmental factors, such as fire and herbivory. Stochastic population growth rates departed from mean matrix population growth rate for temporally autocorrelated environments. Our findings indicate that demographic data and models of closely related plant species cannot necessarily be used to make recommendations for conservation or control, and that post-seedling survival and the sequence of environmental conditions are critical for determining plant population growth rate., authorCount :10

Kent, C. S., Bouchet, P., Wellard, R., Parnum, I., Fouda, L., & Erbe, C. (2020). Seasonal productivity drives aggregations of killer whales and other cetaceans over submarine canyons of the Bremer Sub-Basin, south-western Australia. Australian Mammalogy, 43(2), 168-178. https://doi.org/10.1071/AM19058