Indicators of regime shifts in ecological systems: what do we need to know and when do we need to know it?

Because novel ecological conditions can cause severe and long-lasting environmental damage with large economic costs, ecologists must identify possible environmental regime shifts and pro-actively guide ecosystem management. As an illustrative example, we apply six potential indicators of impending regime shifts to Carpenter and Brock’s (2006) model of lake eutrophication and analyze whether or not they afford adequate advance warning to enable preventative interventions. Our initial analyses suggest that an indicator based on the high-frequency signal in the spectral density of the time-series provides the best advance warning of a regime shift, even when only incomplete information about underlying system drivers and processes is available. In light of this result, we explore two key factors associated with using indicators to prevent regime shifts. The first key factor is the amount of inertia in the system – how fast the system will react to a change in management, given that a manager can actually control relevant system drivers. If rapid, intensive management is possible, our analyses suggest that an indicator must provide at least 20 years advance warning to reduce the probability of a regime shift to < 5%. As time to, or intensity of, intervention is increased, the necessary amount of advance warning required to avoid a regime shift increases exponentially. The second key factor concerns the amount and type of variability intrinsic to the system, and the impact of this variability on the power of an indicator. Indicators are considered powerful if they detect an impending regime shift with adequate lead time for effective management intervention but not so far in advance that interventions are too costly or unnecessary. Intrinsic “noise” in the system obscures the “signal” provided by all indicators and therefore power of the indicators declines rapidly with increasing within- and between-year variability in measurable variables or parameters. Our results highlight the key role of human decisions in managing ecosystems and the importance of pro-active application of the precautionary principle to avoid regime shifts.Organismic and Evolutionary BiologyOther Research Uni

Disease and the Dynamics of Food Webs

What models and statistical tools can best help us assess how ecosystems respond to the impact of multiple factors, such as disease, predation, fire, and rain

Plasmodium falciparum: Differential Selection of Drug Resistance Alleles in Contiguous Urban and Peri-Urban Areas of Brazzaville, Republic of Congo

The African continent is currently experiencing rapid population growth, with rising urbanization increasing the percentage of the population living in large towns and cities. We studied the impact of the degree of urbanization on the population genetics of Plasmodium falciparum in urban and peri-urban areas in and around the city of Brazzaville, Republic of Congo. This field setting, which incorporates local health centers situated in areas of varying urbanization, is of interest as it allows the characterization of malaria parasites from areas where the human, parasite, and mosquito populations are shared, but where differences in the degree of urbanization (leading to dramatic differences in transmission intensity) cause the pattern of malaria transmission to differ greatly. We have investigated how these differences in transmission intensity affect parasite genetic diversity, including the amount of genetic polymorphism in each area, the degree of linkage disequilibrium within the populations, and the prevalence and frequency of drug resistance markers. To determine parasite population structure, heterozygosity and linkage disequilibrium, we typed eight microsatellite markers and performed haplotype analysis of the msp1 gene by PCR. Mutations known to be associated with resistance to the antimalarial drugs chloroquine and pyrimethamine were determined by sequencing the relevant portions of the crt and dhfr genes, respectively. We found that parasite genetic diversity was comparable between the two sites, with high levels of polymorphism being maintained in both areas despite dramatic differences in transmission intensity. Crucially, we found that the frequencies of genetic markers of drug resistance against pyrimethamine and chloroquine differed significantly between the sites, indicative of differing selection pressures in the two areas

Allelic Diversity of the Plasmodium falciparum Erythrocyte Membrane Protein 1 Entails Variant-Specific Red Cell Surface Epitopes

The clonally variant Plasmodium falciparum PfEMP1 adhesin is a virulence factor and a prime target of humoral immunity. It is encoded by a repertoire of functionally differentiated var genes, which display architectural diversity and allelic polymorphism. Their serological relationship is key to understanding the evolutionary constraints on this gene family and rational vaccine design. Here, we investigated the Palo Alto/VarO and IT4/R29 and 3D7/PF13_003 parasites lines. VarO and R29 form rosettes with uninfected erythrocytes, a phenotype associated with severe malaria. They express an allelic Cys2/group A NTS-DBL1α1 PfEMP1 domain implicated in rosetting, whose 3D7 ortholog is encoded by PF13_0003. Using these three recombinant NTS-DBL1α1 domains, we elicited antibodies in mice that were used to develop monovariant cultures by panning selection. The 3D7/PF13_0003 parasites formed rosettes, revealing a correlation between sequence identity and virulence phenotype. The antibodies cross-reacted with the allelic domains in ELISA but only minimally with the Cys4/group B/C PFL1955w NTS-DBL1α. By contrast, they were variant-specific in surface seroreactivity of the monovariant-infected red cells by FACS analysis and in rosette-disruption assays. Thus, while ELISA can differentiate serogroups, surface reactivity assays define the more restrictive serotypes. Irrespective of cumulated exposure to infection, antibodies acquired by humans living in a malaria-endemic area also displayed a variant-specific surface reactivity. Although seroprevalence exceeded 90% for each rosetting line, the kinetics of acquistion of surface-reactive antibodies differed in the younger age groups. These data indicate that humans acquire an antibody repertoire to non-overlapping serotypes within a serogroup, consistent with an antibody-driven diversification pressure at the population level. In addition, the data provide important information for vaccine design, as production of a vaccine targeting rosetting PfEMP1 adhesins will require engineering to induce variant-transcending responses or combining multiple serotypes to elicit a broad spectrum of immunity

Impact of SARS-CoV-2 vaccination of children ages 5–11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021–March 2022: A multi-model study

Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments)

Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub

Background AU Coronavirus Disease 2019 (COVID-19) continues to cause :significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). Methods and findings The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. Conclusions COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections