Inferring stochastic group interactions within structured populations via coupled autoregression.

The internal behaviour of a population is an important feature to take account of when modelling its dynamics. In line with kin selection theory, many social species tend to cluster into distinct groups in order to enhance their overall population fitness. Temporal interactions between populations are often modelled using classical mathematical models, but these sometimes fail to delve deeper into the, often uncertain, relationships within populations. Here, we introduce a stochastic framework that aims to capture the interactions of animal groups and an auxiliary population over time. We demonstrate the model's capabilities, from a Bayesian perspective, through simulation studies and by fitting it to predator-prey count time series data. We then derive an approximation to the group correlation structure within such a population, while also taking account of the effect of the auxiliary population. We finally discuss how this approximation can lead to ecologically realistic interpretations in a predator-prey context. This approximation also serves as verification to whether the population in question satisfies our various assumptions. Our modelling approach will be useful for empiricists for monitoring groups within a conservation framework and also theoreticians wanting to quantify interactions, to study cooperation and other phenomena within social populations.

Examining individual learning patterns using generalised linear mixed models.

Everyone learns differently, but individual performance is often ignored in favour of a group-level analysis. Using data from four different experiments, we show that generalised linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns. Producing ellipsoids and cluster analyses based on predicted random effects, individual learning patterns can be identified, clustered and used for comparisons across various experimental conditions or groups. This analysis can handle a range of datasets including discrete, continuous, censored and non-censored, as well as different experimental conditions, sample sizes and trial numbers. Using this approach, we show that learning a face-named paired associative task produced individuals that can learn quickly, with the performance of some remaining high, but with a drop-off in others, whereas other individuals show poor performance throughout the learning period. We see this more clearly in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals who produced a rapid learning and another showing a slow and gradual learning pattern. Using data from another spatial learning task (Sea Hero Quest), we show that individuals' performance generally reflects their age category, but not always. Overall, using this analytical approach may help practitioners in education and medicine to identify those individuals who might need extra help and attention. In addition, identifying learning patterns may enable further investigation of the underlying neural, biological, environmental and other factors associated with these individuals.

Weathering the hunt: The role of barometric pressure in predator insects' foraging behaviour.

Abiotic factors strongly influence ecological interactions and the spatial distribution of organisms. Despite the essential role of barometric pressure, its influence on insect behaviour remains poorly understood, particularly in predators. The effect of barometric pressure variation can significantly impact biological control programs involving entomophagous insects, as they must efficiently allocate time and energy to search for prey in challenging environments. We investigated how predatory insects from different taxonomic groups (Coleoptera, Dermaptera and Neuroptera) adapt their foraging behaviour in response to variations in barometric pressure (low, medium and high). We also examined the response of different life stages to changes in pressure regimes during foraging activities. Our results showed that the searching time of Doru luteipes (Dermaptera: Forficulidae) was faster in a favourable high-pressure regime, whereas Chrysoperla externa (Neuroptera: Chrysopidae) and Eriopis connexa (Coleoptera: Coccinellidae) had similar searching times under varying pressure regimes. Although no differences in prey feeding time were observed among the studied species, the consumption rate was influenced by low barometric pressure leading to a decrease in the number of preyed eggs. Moreover, we provide novel insights into how hemimetabolous (D. luteipes) and holometabolous (E. connexa) species at different life stages respond to barometric pressure. Doru luteipes nymphs and adults had similar consumption rates across all pressure regimes tested, whereas E. connexa larvae consumed fewer eggs under low barometric pressure, but adults were unaffected. This highlights the importance of investigating how abiotic factors affect insects foraging efficiency and predator-prey interactions. Such studies are especially relevant in the current context of climate change, as even subtle changes in abiotic factors can have strong effects on insect behaviour. Barometric pressure is a key meteorological variable that serve as a warning signal for insects to seek shelter and avoid exposure to weather events that could potentially increase their mortality. Understanding the effects of barometric pressure on predatory insects' behaviour can help us develop more effective pest management strategies and promote the resilience of agroecosystems. We provide new insights into the complex relationship between barometric pressure and predator-prey interactions.

Health asymmetry as a predictor of depressive symptomatology over time among older European adults: A growth curve analysis.

OBJECTIVE: Subjective (SH) and objective health (OH) measures are associated with depressive symptomatology in older adults. We explored whether the discrepancy between SH and OH (operationalised as 'health asymmetry' with four categories: 'health optimistic', 'health pessimistic' and 'good health realistic' and 'poor health realistic') may also relate to depressive symptomatology 1) cross-sectionally, and 2) longitudinally, among older European adults. METHODS: Adults (n = 26,520), aged 50+, from 11 European countries, were assessed over six waves of data collection (2006-2020) in the SHARE study. A hierarchical multi-level growth curve model explored whether health asymmetry was associated with depressive symptomology at baseline, and with depressive symptom trajectories across time, accounting for country of origin. RESULTS: At baseline, 11.8% of older adults were classified as health pessimistic, with 15.5% being health optimistic, 42.9% being poor health realistic and 29.8% being good health realistic. A positive linear trend in depressive symptomatology was noted across 14 years of SHARE data (ß = 0.11, p < .001). Health pessimists displayed higher levels of depressive symptoms than both health realistic groups and health optimists. However, health pessimists experienced a less steep increase in depressive symptoms across time (ß = -0.10, p < .001), relative to good health realists. CONCLUSION: Health pessimists experience elevated levels of depressive symptoms, but show less growth in depressive symptomatology than expected. Further research is required to understand the underlying causes of the varying depressive symptom trajectories among these groups.

A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships.

In grassland ecosystems, it is well known that increasing plant species diversity can improve ecosystem functions (i.e., ecosystem responses), for example, by increasing productivity and reducing weed invasion. Diversity-Interactions models use species proportions and their interactions as predictors in a regression framework to assess biodiversity and ecosystem function relationships. However, it can be difficult to model numerous interactions if there are many species, and interactions may be temporally variable or dependent on spatial planting patterns. We developed a new Diversity-Interactions mixed model for jointly assessing many species interactions and within-plot species planting pattern over multiple years. We model pairwise interactions using a small number of fixed parameters that incorporate spatial effects and supplement this by including all pairwise interaction variables as random effects, each constrained to have the same variance within each year. The random effects are indexed by pairs of species within plots rather than a plot-level factor as is typical in mixed models, and capture remaining variation due to pairwise species interactions parsimoniously. We apply our novel methodology to three years of weed invasion data from a 16-species grassland experiment that manipulated plant species diversity and spatial planting pattern and test its statistical properties in a simulation study.Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00505-2.

Logistic versus linear regression-based reliable change index: A simulation study with implications for clinical studies with different sample sizes.

The linear regression-based reliable change index (RCI) is widely used to identify memory impairments through longitudinal assessment. However, the minimum sample size required for estimates to be reliable has never been specified. Using data from 920 participants from the Alzheimer's Disease Neuroimaging Initiative data as true parameters, we run 12,000 simulations for samples of size 10-1,000 and analyzed the percentage of times the estimates are significant, their coverage rate, and the accuracy of the models including both the true-positive rate and the true-negative rate. We compared the linear RCI with a logistic RCI for discrete, bounded scores. We found that the logistic RCI is more accurate than the linear RCI overall, with the linear RCI approximating the logistic RCI for samples of size 200 or greater. We provide an R package to compute the logistic RCI, which can be downloaded from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/LogisticRCI/, and the code to reproduce all results in this article at https://github.com/rafamoral/LogisticRCIpaper/. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Opportunities for Improved Potato Late Blight Management in the Republic of Ireland: Field Evaluation of the Modified Irish Rules Crop Disease Risk Prediction Model.

Potato late blight remains the most significant disease threat of potato cultivation globally, often requiring expensive, time-consuming, and environmentally unfriendly approaches to disease management. The goal of this research was to evaluate whether an estimation of potato late blight risk based on environmental factors can be reliably used to adjust the standard potato late blight management practices and the role of cultivar resistance under growing conditions and contemporary Phytophthora infestans populations in the Republic of Ireland. The modified Irish Rules model made it possible to reduce fungicide usage by 58.7% on average, compared with current standard practices used by growers and without adversely compromising disease control and yield, with similar results achieved by the half-dose program. Host resistance levels were found to be correlated with a delay in the initiation of the epidemics, final foliar disease levels, and reduction of fungicide usage. Disease levels on the highly resistant cultivars remained low, and a clear selection pattern toward the P. infestans genotypes EU_13_A2 and EU_6_A1 was observed. An increase in the frequency of strains belonging to genotypes EU_13_A2 and EU_6_A1 was also observed to occur in the latter part of the trial growing seasons. Because of the increasingly dynamic nature of the population structure, associated with the continued evolution of the P. infestans population and the arrival of EU_36_A2 in the Republic of Ireland, routine population monitoring is necessary to ensure that potato late blight control strategies remain effective.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Data on morphological features of mycosis induced by Colletotrichum nymphaeae and Lecanicillium longisporum on citrus orthezia scale.

We describe symptoms of mycosis induced by two native fungal entomopathogens of the citrus orthezia scale, Praelongorthezia praelonga (Hemiptera: Ortheziidae), an important pest of citrus orchards. The data presented in this article are related to the article entitled "Seasonal prevalence of the insect pathogenic fungus Colletotrichum nymphaeae in Brazilian citrus groves under different chemical pesticide regimes" [1]. The endemic fungal pathogen, C. nymphaeae, emerges through the thin cuticular intersegmental regions of the citrus orthezia scale body revealing orange salmon-pigmented conidiophores bearing conidial masses, as well as producing rhizoid-like hyphae that extend over the citrus leaf. By contrast, nymphs or adult females of this scale insect infected with Lecanicillium longisporum exhibit profuse outgrowth of bright white-pigmented conidiophores with clusters of conidia emerging from the insect intersegmental membranes, and mycosed cadavers are commonly observed attached to the leaf surface by hyphal extensions. These morphological differences are important features to discriminate these fungal entomopathogens in citrus orthezia scales.

The effect of synthetic pesticides and sulfur used in conventional and organically grown strawberry and soybean on Neozygites floridana, a natural enemy of spider mites.

BACKGROUND: The beneficial fungus Neozygites floridana kills the two-spotted spider mite Tetranychus urticae, which is a serious polyphagous plant pest worldwide. Outbreaks of spider mites in strawberry and soybean have been associated with pesticide applications. Pesticides may affect N. floridana and consequently the natural control of T. urticae. N. floridana is a fungus difficult to grow in artificial media, and for this reason, very few studies have been conducted with this fungus, especially regarding the impact of pesticides. The aim of this study was to conduct a laboratory experiment to evaluate the effect of pesticides used in strawberry and soybean crops on N. floridana. RESULTS: Among the pesticides used in strawberry, the fungicides sulfur and cyprodinil + fludioxonil completely inhibited both the sporulation and conidial germination of N. floridana. The fungicide fluazinam affected N. floridana drastically. The application of the fungicide tebuconazole and the insecticides fenpropathrin and abamectin resulted in a less pronounced negative effect on N. floridana. Except for epoxiconazole and cyproconazole, all tested fungicides used in soybean resulted in a complete inhibition of N. floridana. Among the three insecticides used in soybean, lambda-cyhalothrin and deltamethrin resulted in a significant inhibition of N. floridana. CONCLUSION: The insecticides/ acaricides abamectin and lambda-cyhalothrin at half concentrations and fenpropathrin and permethrin and the fungicide tebuconazole at the recommended concentrations resulted in the lowest impact on N. floridana. The fungicides with the active ingredients sulfur, cyprodinil + fludioxonil, azoxystrobin, azoxystrobin + cyproconazole, trifloxystrobin + tebuconazole and pyraclostrobin + epoxiconazole negatively affected N. floridana. © 2015 Society of Chemical Industry.