Range and extinction dynamics of the steppe bison in Siberia : A pattern-oriented modelling approach

Aim To determine the ecological processes and drivers of range collapse, population decline and eventual extinction of the steppe bison in Eurasia. Location Siberia. Time period Pleistocene and Holocene. Major taxa studied Steppe bison (Bison priscus). Methods We configured 110,000 spatially explicit population models (SEPMs) of climate-human-steppe bison interactions in Siberia, which we ran at generational time steps from 50,000 years before present. We used pattern-oriented modelling (POM) and fossil-based inferences of distribution and demographic change of steppe bison to identify which SEPMs adequately simulated important interactions between ecological processes and biological threats. These "best models" were then used to disentangle the mechanisms that were integral in the population decline and later extinction of the steppe bison in its last stronghold in Eurasia. Results Our continuous reconstructions of the range and extinction dynamics of steppe bison were able to reconcile inferences of spatio-temporal occurrence and the timing and location of extinction in Siberia based on hundreds of radiocarbon-dated steppe bison fossils. We showed that simulating the ecological pathway to extinction for steppe bison in Siberia in the early Holocene required very specific ecological niche constraints, demographic processes and a constrained synergy of climate and human hunting dynamics during the Pleistocene-Holocene transition. Main conclusions Ecological processes and drivers that caused ancient population declines of species can be reconstructed at high spatio-temporal resolutions using SEPMs and POM. Using this approach, we found that climatic change and hunting by humans are likely to have interacted with key ecological processes to cause the extinction of the steppe bison in its last refuge in Eurasia.Peer reviewe

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Revealing ecological processes of range dynamics through space and time

In ecology, process-explicit models represent the dynamics of ecological systems as explicit functions of the mechanisms and drivers that produced them. Process-explicit models are therefore able to link observed ecological patterns, such as species spatial abundance patterns, directly to their causes, such as climate and environmental change. In this PhD thesis, I show how processexplicit models can be used to establish determinants of range collapses and extinction by unpacking complex interactions between ecological lifestyles, biological traits, climate change, and human-driven threats. By providing a more complete understanding of the ecological mechanisms that regulate species’ responses to climate and environmental change, my PhD research provides information needed to better predict vulnerability to future climate and environmental change. In Chapter I, I reviewed and interpreted the techniques used to unlock ecological and evolutionary mechanisms responsible for spatial and temporal patterns of biodiversity ranging from the gene to the ecosystem. By revealing how models can codify the generalisable mechanisms responsible for the distributions of life on Earth, this review will help to enable important advances in macroecology, evolutionary biogeography and conservation biology, strengthening both basic and applied science. Chapter II is a sensitivity analysis of the Climate Informed Spatial Genetic Model (CISGeM), a process-explicit model of human migration out of Africa. While it is well-known that correlative models of species ranges, such as environmental niche models, are highly sensitive to the climate dataset used for parameterisation, the sensitivity of process-explicit models of human migration to climate data and other model parameters has never been tested. I found that the outputs of CISGeM are robust to the choice of palaeoclimate simulation data, but sensitive to the values for key demographic processes. In Chapter III, I used process-explicit models to reconstruct the late Quaternary range dynamics of the steppe bison (Bison priscus) using a new R package, paleopop, that I co-developed. The approach linked spatially explicit population models with inferences of demographic change from fossils and ancient DNA to continuously simulate 45,000 years of steppe bison extinction dynamics. The models included dispersal and demographic processes responding to human harvesting and rapid deglacial warming. I found that deglacial warming interacted with hunting pressure from humans to cause the range of the steppe bison to contract to refugial highland populations, which became extinct in the early Holocene. Chapter IV used a related approach to reconstruct the range dynamics of the European bison (Bison bonasus) from the last ice age to the year 1500. The European bison became extinct in the wild in 1927 and has been bred back from captive animals. It is a goal of European Union policy to reintroduce the bison more broadly, but there is a debate about the optimal locations and habitats for reintroduction. I inform this debate by showing where bison became extinct due to hunting, land use change, and climate change. General findings from my PhD will help macroecologists to better model and understand species range and extinction dynamics, providing important theoretical and applied insights for conserving vulnerable species in the Anthropocene.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202

Association between Preexisting Mental Health Disorders and Adverse Outcomes in Adult Intensive Care Patients:A Data Linkage Study

OBJECTIVES: Mental illness is known to adversely affect the physical health of patients in primary and acute care settings; however, its impact on critically ill patients is less well studied. This study aimed to determine the prevalence, characteristics, and outcomes of patients admitted to the ICU with a preexisting mental health disorder. DESIGN: A multicenter, retrospective cohort study using linked data from electronic ICU clinical progress notes and the Australia and New Zealand Intensive Care Society Adult Patient Database. SETTING/PATIENTS: All patients admitted to eight Australian adult ICUs in the calendar year 2019. Readmissions within the same hospitalization were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Natural language processing techniques were used to classify preexisting mental health disorders in participants based on clinician documentation in electronic ICU clinical progress notes. Sixteen thousand two hundred twenty-eight patients (58% male) were included in the study, of which 5,044 (31.1%) had a documented preexisting mental health disorder. Affective disorders were the most common subtype occurring in 2,633 patients (16.2%), followed by anxiety disorders, occurring in 1,611 patients (9.9%). Mixed-effects regression modeling found patients with a preexisting mental health disorder stayed in ICU 13% longer than other patients (β-coefficient, 0.12; 95% CI, 0.10-0.15) and were more likely to experience invasive ventilation (odds ratio, 1.42; 95% CI, 1.30-1.56). Severity of illness and ICU mortality rates were similar in both groups. CONCLUSIONS: Patients with preexisting mental health disorders form a significant subgroup within the ICU. The presence of a preexisting mental health disorder is associated with greater ICU length of stay and higher rates of invasive ventilation, suggesting these patients may have a different clinical trajectory to patients with no mental health history. Further research is needed to better understand the reasons for these adverse outcomes and to develop interventions to better support these patients during and after ICU admission.</p

Development and validation of a risk score to predict unplanned hospital readmissions in ICU survivors:A data linkage study

Background: Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service. Objectives: The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services. Methods: A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation. Results: 12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40–1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39–1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14–2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67–0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups—high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died). Conclusions: Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.</p

Process-explicit simulations of European bison abundance in Eurasia from 21,000 years ago to 1500 CE

We simulated the abundance and range dynamics of the European bison in Eurasia from 21,000 years ago to 1500 CE using spatially explicit population models. We validated the models through rounds of pattern-oriented modelling to ensure that the models accurately replicated observed ecological patterns. These data represent a weighted mean of the best 25 models of European bison range dynamics selected by pattern-oriented modeling. This multi-band raster has one layer for each 10-year timestep from 21,000 years ago to 1500 CE. Each layer is a spatially explicit representation of the European bison's range and abundance at that timestep.  These are provided as GeoTIFF files in a Lambert Azimuthal Equal Area projection centred on a reference latitude of 57.5°N and a reference longitude of 25°E with a resolution of 86.6 by 75.6 km.  Grid cells with zero bison are potentially occupiable cells (not covered by ice sheets or ocean) with no bison occupancy. Grid cells marked NA are not valid occupiable cells at that time point, either because it is covered by glacial ice or because of the sea level.</p

Using paleo-archives to safeguard biodiversity under climate change

International audienc