Ecology and bioenergetics of the gudgeon (Hypseleotris spp.) in Maroon Dam: a zooplanktivorous fish in a whole-lake biomanipulation

Gudgeon (Hypseleotris spp.) are the most widespread and abundant native Australian freshwater fish and the dominant zooplanktivore in Maroon Dam, the site of Australia's first whole-lake biomanipulation experiment. The spatial (littoral and pelagic) and temporal (diurnal and seasonal) distribution and diet of Hypseleotris was examined following the addition of 100,000 piscivorous Australian Bass (Macquaria novemaculeata) to Maroon Dam in the summer of 1998/99. A strong spatial and temporal ontogeny was observed, with smaller (20 mm SL) remaining in the littoral throughout the day and night. Spatial ontogeny affected diet also, with fish consuming a decreasing proportion of zooplankton and an increasing proportion of macro-invertebrates as fish length increased and habitat use changed. A bioenergetics model was constructed to examine these distribution and diet patterns. Laboratory derived consumption and respiration parameters were combined with caloric densities and commonly accepted excretion and activity scalars to produce modeled growth estimates that were validated against Hypseleotris age-at-growth data collected from a diversity of habitats. Using this model, it was concluded that the spatial and temporal ontogeny and diet of Hypseleotris in Maroon Dam described the most energetically advantageous life history. Unlike many zooplanktivores in northern hemisphere lakes, Hypseleotris did not appear to engage in migratory predator avoidance behaviour. This is discussed in a context of Australia's paucity of pelagic piscivores. It is concluded that top-down biomanipulation by stocking of native piscivores has only a limited application in Australia, and that other biomanipulation techniques may prove more successful

Contrasting patterns of larval mortality in two sympatric riverine fish species: A test of the critical period hypothesis

Understanding the causal mechanisms that determine recruitment success is critical to the effective conservation of wild fish populations. Although recruitment strength is likely determined during early life when mortality is greatest, few studies have documented age-specific mortality rates for fish during this period. We investigated age-specific mortality of individual cohorts of two species of riverine fish from yolksac larvae to juveniles, assaying for the presence of a "critical period": A time when mortality is unusually high. Early life stages of carp gudgeons (Hypseleotris spp.) and unspecked hardyhead (Craterocephalus stercusmuscarum fulvus)-two fishes that differ in fecundity, egg size and overlap between endogenous and exogenous feeding-were collected every second day for four months. We fitted survivorship curves to 22 carp gudgeon and 15 unspecked hardyhead four-day cohorts and tested several mortality functions. Mortality rates declined with age for carp gudgeon, with mean instantaneous mortality rates (-Z) ranging from 1.40-0.03. In contrast, mortality rates for unspecked hardyhead were constant across the larval period, with a mean -Z of 0.15. There was strong evidence of a critical period for carp gudgeon larvae from hatch until 6 days old, and no evidence of a critical period for unspecked hardyhead. Total larval mortality for carp gudgeon and unspecked hardyhead up to 24 days of age was estimated to be 97.8 and 94.3%, respectively. We hypothesise that life history strategy may play an important role in shaping overall mortality and the pattern of mortality during early life in these two fishes

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Individual cohort survivorship curves for carp gudgeon larvae in the Lindsay River between October 2005 and February 2006.

<p>Data log_e (x+1) transformed. Black dotted line  =  Weibull function (non-constant mortality model), black solid line  =  asymptote function (non-constant mortality model), and grey solid line  =  linear function (constant mortality model). Age class (days) represent 2 day groupings of larvae (e.g. 2 = 1-2 day old larvae, 4 = 3–4 day old larvae etc).</p

AICc results for the alternative models of mortality during the larval phase of carp gudgeon and unspecked hardyhead for each 4d cohort; linear (constant mortality (Z)), asymptote (non-constant mortality (Z)) and Weibull (non-constant mortality (Z)) functions.

<p>Bold = model of best fit. CP  =  critical period, where Y = yes, N = no,? =  could not be determined.</p><p>AICc results for the alternative models of mortality during the larval phase of carp gudgeon and unspecked hardyhead for each 4d cohort; linear (constant mortality (Z)), asymptote (non-constant mortality (Z)) and Weibull (non-constant mortality (Z)) functions.</p

Map of the study area; Lindsay River, Victoria, Australia.

<p>Map of the study area; Lindsay River, Victoria, Australia.</p

Mean (±SE) cohort; instantaneous mortality rates (-<i>Z</i>), daily mortality rates (M_daily) and cumulative survival (%) of carp gudgeon and unspecked hardyhead larvae.

<p>Age class (days) represent 2 day groupings of larvae (e.g. 2 = 1–2 day old larvae, 4 = 3–4 day old larvae etc).</p

Individual cohort survivorship curves for unspecked hardyhead larvae in the Lindsay River between October 2005 and February 2006.

<p>Data log_e (x+1) transformed. Black dotted line  =  Weibull function (non-constant mortality model), black solid line  =  asymptote function (non-constant mortality model), and grey solid line  =  linear function (constant mortality model). Age class (days) represent 2 day groupings of larvae (e.g. 2 = 1–2 day old larvae, 4 = 3–4 day old larvae etc).</p