Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades.

Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.

Topological clustering of multilayer networks.

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness.

The recurring impact of storm disturbance on black sea bass (Centropristis striata) movement behaviors in the Mid-Atlantic Bight.

Storm events are a significant source of disturbance in the Middle Atlantic Bight, in the Northwest Atlantic, that cause rapid destratification of the water column during the late summer and early fall. Storm-driven mixing can be considered as a seasonal disturbance regime to demersal communities, characterized by the recurrence of large changes in bottom water temperatures. Black sea bass are a model ubiquitous demersal species in the Middle Atlantic Bight, as their predominantly sedentary behavior makes them ideal for tagging studies while also regularly exposing them to summer storm disturbances and the physiological stresses associated with thermal destratification.

Effects of intense storm events on dolphin occurrence and foraging behavior.

As storms become increasingly intense and frequent due to climate change, we must better understand how they alter environmental conditions and impact species. However, storms are ephemeral and provide logistical challenges that prevent visual surveys commonly used to understand marine mammal ecology. Thus, relatively little is known about top predators' responses to such environmental disturbances.

Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA.

Influenza is one of the main causes of death, not only in the USA but worldwide. Its significant economic and public health impacts necessitate development of accurate and efficient algorithms for forecasting of any upcoming influenza outbreaks. Most currently available methods for influenza prediction are based on parametric time series and regression models that impose restrictive and often unverifiable assumptions on the data.

Empirical evidence that large marine predator foraging behavior is consistent with area-restricted search theory.

When prey is patchily distributed, predators are expected to spend more time searching for food in proximity of recent prey captures before searching in other areas. This behavior, known as area-restricted search, results in predators remaining localized in areas where prey had been detected previously because of the higher probability of encountering additional prey. However, few studies have tested these predictions on marine species because of the difficulties of observing feeding behavior.

Detection of the effects of stormwater control measure in streams using a Bayesian BACI power analysis.

The unpredictable timing and magnitude of precipitation events and the spatiotemporal variability of constituent concentrations are major complications to effective monitoring of watershed nutrient and sediment loads. Furthermore, detecting small changes in constituent loads in response to implementation of Stormwater control measures (SCMs) against natural variability is a challenge. Nevertheless, regulatory frameworks that direct reductions of pollutants to streams frequently depend on the ability to quantify changes in loads after management interventions.

Time series models of decadal trends in the harmful algal species Karlodinium veneficum in Chesapeake Bay.

The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time.

Bootstrap quantification of estimation uncertainties in network degree distributions.

We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are assumed to be unknown. The key idea is based on adaptation of the "blocking" argument, developed for bootstrapping of time series and re-tiling of spatial data, to random networks.

Year-round spatiotemporal distribution of harbour porpoises within and around the Maryland wind energy area.

Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions.