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Evaluating the performance of multivariate indicators of resilience loss.

ABSTRACT: Various complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These 'tipping points' are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

SUBMITTER: Weinans E

PROVIDER: S-EPMC8080839 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135-0.250), Ra (0.008-0.058), and Ske (0.001-0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

Project description:BackgroundTo build capacity in medicines management, the Uganda Ministry of Health introduced a nationwide supervision, performance assessment and recognition strategy (SPARS) in 2012. Medicines management supervisors (MMS) assess performance using 25 indicators to identify problems, focus supervision, and monitor improvement in medicines stock and storage management, ordering and reporting, and prescribing and dispensing. Although the indicators are well-recognized and used internationally, little was known about the reliability of these indicators. An initial assessment of inter-rater reliability (IRR), which measures agreement among raters (i.e., MMS), showed poor IRR; subsequently, we implemented efforts to improve IRR. The aim of this study was to assess IRR for SPARS indicators at two subsequent time points to determine whether IRR increased following efforts to improve reproducibility.MethodsIRR was assessed in 2011 and again after efforts to improve IRR in 2012 and 2013. Efforts included targeted training, providing detailed guidelines and job aids, and refining indicator definitions and response categories. In the assessments, teams of three MMS measured 24 SPARS indicators in 26 facilities. We calculated IRR as a team agreement score (i.e., percent of the MMS teams in which all three MMS had the same score). Two sample tests for proportions were used to compare IRR scores for each indicator, domain, and overall for the initial assessment and the following two assessments. We also compared the IRR scores for indicators classified as simple (binary) versus complex (multi-component). Logistic regression was used to identify supervisor group characteristics associated with domain-specific and overall IRR scores.ResultsInitially only five (21%) indicators had acceptable reproducibility, defined as an IRR score ≥ 75%. At the initial assessment, prescribing quality indicators had the lowest and stock management indicators had the highest IRR. By the third IRR assessment, 12 (50%) indicators had acceptable reproducibility, and the overall IRR score improved from 57% to 72%. The IRR of simple indicators was consistently higher than that of complex indicators in the three assessment periods. We found no correlation between IRR scores and MMS experience or professional background.ConclusionsAssessments of indicator reproducibility are needed to improve IRR. Using simple indicators is recommended.

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Evaluating the performance of multivariate indicators of resilience loss.

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