Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient.

Identification of the key biotic and abiotic drivers within food webs is important for understanding species abundance changes in ecosystems, particularly across ecotones where there may be strong variation in interaction strengths. Using structural equation models (SEMs) and four decades of integrated data from the San Francisco Estuary, we investigated the relative effects of top-down, bottom-up, and environmental drivers on multiple trophic levels of the pelagic food web along an estuarine salinity gradient and at both annual and monthly temporal resolutions. We found that interactions varied across the estuarine gradient and that the detectability of different interactions depended on timescale. For example, for zooplankton and estuarine fishes, bottom-up effects appeared to be stronger in the freshwater upstream regions, while top-down effects were stronger in the brackish downstream regions. Some relationships (e.g., bottom-up effects of phytoplankton on zooplankton) were seen primarily at annual timescales, whereas others (e.g., temperature effects) were only observed at monthly timescales. We also found that the net effect of environmental drivers was similar to or greater than bottom-up and top-down effects for all food web components. These findings can help identify which trophic levels or environmental factors could be targeted by management actions to have the greatest impact on estuarine forage fishes and the spatial and temporal scale at which responses might be observed. More broadly, this study highlights how environmental gradients can structure community interactions and how long-term data sets can be leveraged to generate insights across multiple scales.

Combining multiple data sources with different biases in state-space models for population dynamics.

The resolution at which animal populations can be modeled can be increased when multiple datasets corresponding to different life stages are available, allowing, for example, seasonal instead of annual descriptions of dynamics. However, the abundance estimates used for model fitting can have multiple sources of error, both random and systematic, namely bias. We focus here on the consequences of, and how to address, differing and unknown observation biases when fitting models.State-space models (SSMs) separate process variation and observation error, thus providing a framework to account for different and unknown estimate biases across multiple datasets. Here we study the effects on the inference of including or excluding bias parameters for a sequential life stage population dynamics SSM using a combination of theory, simulation experiments, and an empirical example.When the data, that is, abundance estimates, are unbiased, including bias parameters leads to increased imprecision compared to a model that correctly excludes bias parameters. But when observations are biased and no bias parameters are estimated, recruitment and survival processes are inaccurately estimated and estimates of process variance become biased high. These problems are substantially reduced by including bias parameters and fixing one of them at even an incorrect value. The primary inferential challenge is that models with bias parameters can show properties of being parameter redundant even when they are not in theory.Combining multiple datasets into a single analysis by using bias parameters to rescale data can offer significant improvements to inference and model diagnostics. Because their estimability in practice is dataset specific and will likely require more precise estimates than might be expected from ecological datasets, we outline some strategies for characterizing process uncertainty when it is confounded by bias parameters.

Ictal asystole: an uncommon but significant cause of transient loss of consciousness-a case series.

Ictal bradycardia and asystole are rare, but potentially serious complications of epileptic seizures. We present a case series of three such patients diagnosed through our syncope service. For two patients, treatment with anticonvulsant therapy alone achieved symptom control. The third patient was initially managed with permanent pacemaker insertion due to detection of a significant ventricular pause. He subsequently developed more pronounced symptoms suggestive of seizure.Ictal asystole can cause significant diagnostic challenge and management strategies remain controversial due to the overlap in presentation to cardiology and neurology services. The number of patients affected is low, impeding the formulation of an evidence base for treatment. We propose multidisciplinary working facilitated by a specialist syncope service as a means of recognising and treating this condition more effectively.

Atypical presentations in the hospitalised older adult testing positive for SARS-CoV-2: a retrospective observational study in Glasgow, Scotland.

Introduction: Understanding of how SARS-CoV-2 manifests itself in older adults was unknown at the outset of the pandemic. We undertook a retrospective observational analysis of all patients admitted to older people's services with confirmed COVID-19 in one of the largest hospitals in Europe. We detail presenting symptoms, prognostic features and vulnerability to nosocomial spread. Methods: We retrospectively collected data for each patient with a positive SARSCoV-2 RT PCR between 18th March and the 20th April 2020 in a department of medicine for the elderly in Glasgow. Results: 222 patients were included in our analysis. Age ranged from 56 to 99 years (mean = 82) and 148 were female (67%). 119 patients had a positive swab for SARS-CoV-2 within the first 14 days of admission, only 32% of these patients presented with primarily a respiratory type illness. 103 patients (46%) tested positive after 14 days of admission - this was felt to represent likely nosocomial infection. 95 patients (43%) died by day 30 after diagnosis. Discussion: This data indicates that older people were more likely to present with non-respiratory symptoms. High clinical frailty scores, severe lymphopenia and cumulative comorbidities were associated with higher mortality rates. Several contributing factors will have led to nosocomial transmission.

Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data.

State-space models (SSMs) are a popular tool for modeling animal abundances. Inference difficulties for simple linear SSMs are well known, particularly in relation to simultaneous estimation of process and observation variances. Several remedies to overcome estimation problems have been studied for relatively simple SSMs, but whether these challenges and proposed remedies apply for nonlinear stage-structured SSMs, an important class of ecological models, is less well understood. Here we identify improvements for inference about nonlinear stage-structured SSMs fit with biased sequential life stage data. Theoretical analyses indicate parameter identifiability requires covariates in the state processes. Simulation studies show that plugging in externally estimated observation variances, as opposed to jointly estimating them with other parameters, reduces bias and standard error of estimates. In contrast to previous results for simple linear SSMs, strong confounding between jointly estimated process and observation variance parameters was not found in the models explored here. However, when observation variance was also estimated in the motivating case study, the resulting process variance estimates were implausibly low (near-zero). As SSMs are used in increasingly complex ways, understanding when inference can be expected to be successful, and what aids it, becomes more important. Our study illustrates (a) the need for relevant process covariates and (b) the benefits of using externally estimated observation variances for inference about nonlinear stage-structured SSMs.

Resistance and resilience of pelagic and littoral fishes to drought in the San Francisco Estuary.

Many estuarine ecosystems and the fish communities that inhabit them have undergone substantial changes in the past several decades, largely due to multiple interacting stressors that are often of anthropogenic origin. Few are more impactful than droughts, which are predicted to increase in both frequency and severity with climate change. In this study, we examined over five decades of fish monitoring data from the San Francisco Estuary, California, USA, to evaluate the resistance and resilience of fish communities to disturbance from prolonged drought events. High resistance was defined by the lack of decline in species occurrence from a wet to a subsequent drought period, while high resilience was defined by the increase in species occurrence from a drought to a subsequent wet period. We found some unifying themes connecting the multiple drought events over the 50-yr period. Pelagic fishes consistently declined during droughts (low resistance), but exhibit a considerable amount of resiliency and often rebound in the subsequent wet years. However, full recovery does not occur in all wet years following droughts, leading to permanently lower baseline numbers for some pelagic fishes over time. In contrast, littoral fishes seem to be more resistant to drought and may even increase in occurrence during dry years. Based on the consistent detrimental effects of drought on pelagic fishes within the San Francisco Estuary and the inability of these fish populations to recover in some years, we conclude that freshwater flow remains a crucial but not sufficient management tool for the conservation of estuarine biodiversity.

Discrete-storm water-table fluctuation method to estimate episodic recharge.

We have developed a method to identify and quantify recharge episodes, along with their associated infiltration-related inputs, by a consistent, systematic procedure. Our algorithm partitions a time series of water levels into discrete recharge episodes and intervals of no episodic recharge. It correlates each recharge episode with a specific interval of rainfall, so storm characteristics such as intensity and duration can be associated with the amount of recharge that results. To be useful in humid climates, the algorithm evaluates the separability of events, so that those whose recharge cannot be associated with a single storm can be appropriately lumped together. Elements of this method that are subject to subjectivity in the application of hydrologic judgment are values of lag time, fluctuation tolerance, and master recession parameters. Because these are determined once for a given site, they do not contribute subjective influences affecting episode-to-episode comparisons. By centralizing the elements requiring scientific judgment, our method facilitates such comparisons by keeping the most subjective elements openly apparent, making it easy to maintain consistency. If applied to a period of data long enough to include recharge episodes with broadly diverse characteristics, the method has value for predicting how climatic alterations in the distribution of storm intensities and seasonal duration may affect recharge.

Refracture following fracture liaison service assessment illustrates the requirement for integrated falls and fracture services.

The Fracture Liaison Service (FLS) allows appropriate antiosteoporosis therapy to be targeted to potentially reduce future fracture risk. A proportion of these treated patients will still experience a further fracture. This work reviews the characteristics of these patients. Data were collated for patients >65 years old presenting to the South Glasgow FLS between January 2001 and August 2004. There were 2,489 patients who presented (incident fracture group), and 129 (5.2%) sustained an additional fracture (refracture group). Median age of the incident fracture group was 77.8 years vs. 80.6 years for the refracture group (P = nonsignificant). The refracture group was determined according to whether their incident fracture was hip (n = 47) or nonhip (n = 82). When the incident fracture was hip, a refracture was more likely to be a further hip fracture (chi(2) = 14.4, P = 0.002) and patients refractured sooner (median time to refracture 194 [range 10-1,134] days vs. 258 [range 6-1,081] days [nonhip]) (P = nonsignificant). In the refracture group, 76% of patients were already on osteoporosis treatment after their incident fracture. Patients over 65 years of age presenting to FLS who sustain an additional fracture are older; are likely to sustain another hip fracture after an incident hip fracture; often refracture early, particularly when the incident fracture is of the hip; and are often already on antiosteoporosis treatment. Therefore, it is important to identify these high-risk patients and offer a combined approach of prompt drug treatment through a systematic and specialist osteoporosis management team along with reducing any reversible falls risk factors.