Biodiversity monitoring for a just planetary future.

Data that influence policy and major investment decisions risk entrenching social and political inequities.

Recognition and Treatment Challenges of Acute Clozapine Withdrawal Syndrome: A Case Report.

The limited psychiatric bedspace due to the COVID-19 pandemic and the lack of access to an up-to-date medication regimen delayed the recognition of the diagnosis and treatment for a 40-year-old man with schizoaffective disorder, bipolar type, who traveled from his home city and abruptly discontinued his prescription of clozapine. He developed a cholinergic rebound syndrome including delirium and extrapyramidal symptoms (EPS). The delay included time spent in two different medical hospitals: one awaiting psychiatric bedspace, and secondly, when the patient's cholinergic rebound syndrome was misdiagnosed as acute alcohol withdrawal. Once the etiology was recognized, he was promptly treated with anticholinergic medication (benztropine) and retitrated to his outpatient dose of clozapine leading to the resolution of symptoms including delirium and EPS. This case will discuss the challenges of continuity of care in delirious, psychotic, or otherwise confused patients, including contributions from the COVID-19 pandemic. A medication card or other improvements in medication databases that may reduce delays in treatment are discussed.

Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset.

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models-especially N-mixtures with beta-binomial detection submodels-were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.

Identifying engaging bird species and traits with community science observations.

Identifying rates at which birders engage with different species can inform the impact and efficacy of conservation outreach and the scientific use of community-collected biodiversity data. Species that are thought to be "charismatic" are often prioritized in conservation, and previous researchers have used sociological experiments and digital records to estimate charisma indirectly. In this study, we take advantage of community science efforts as another record of human engagement with animals that can reveal observer biases directly, which are in part driven by observer preference. We apply a multistage analysis to ask whether opportunistic birders contributing to iNaturalist engage more with larger, more colorful, and rarer birds relative to a baseline approximated from eBird contributors. We find that body mass, color contrast, and range size all predict overrepresentation in the opportunistic dataset. We also find evidence that, across 472 modeled species, 52 species are significantly overreported and 158 are significantly underreported, indicating a wide variety of species-specific effects. Understanding which birds are highly engaging can aid conservationists in creating impactful outreach materials and engaging new naturalists. The quantified differences between two prominent community science efforts may also be of use for researchers leveraging the data from one or both of them to answer scientific questions of interest.

A comparison of private and public dental students' perceptions of extramural programming.

This project was undertaken to compare the opinions of private and public dental school students' perceptions concerning extramural programming, which is defined as any aspect of the curriculum in which undergraduate dental students provide dental care outside the main dental facility. A survey instrument was used to collect data from undergraduate students at a private (N = 267; 88.4 percent response rate) and at a public (N = 213; 67.2 percent response rate) dental school. When asked to rate the value of various extramural sites in making them a better dentist, both groups rated private dental offices the most valuable and prisons the least valuable. When questioned about the amount of time students should spend each year in extramural programming, private students, overall, desired 34 percent more time than did public students. When asked what percentage of the total time spent in extramural programming students should spend providing various categories of dental care, public school students thought 26 percent more time should be spent rendering preventive services/health education than did the private students. The private students indicated a stronger desire (13 percent more) for rendering clinical services than did public students. Both private and public students were most likely to enter group private practice after graduation. The increasing interest in community-based programs makes the information gained from this study useful for future curriculum planning.