Quantifying Planococcus ficus (Hemiptera: Pseudococcidae) Invasion in Northern California Vineyards to Inform Management Strategy

2020 ◽  
Author(s):  
Tyler E Schartel ◽  
Monica L Cooper ◽  
Aubrey May ◽  
Matthew P Daugherty
Keyword(s):  
Habitat Suitability ◽  
Regression Tree ◽  
Negative Effects ◽  
Boosted Regression Tree ◽  
Study Region ◽  
Planococcus Ficus ◽  
Habitat Suitability Modeling ◽  
Napa County ◽  
Forest Modeling ◽  
Positive Effect

Abstract The spread and impact of invasive species in exotic ranges can be mitigated by increased understanding of pest invasion dynamics. Here, we used geospatial analyses and habitat suitability modeling to characterize the invasion of an important vineyard pest, vine mealybug (Planococcus ficus Signoret, Hemiptera: Pseudococcidae), using nearly 15,000 trapping records from throughout Napa County, California, between 2012 and 2017. Spatial autocorrelation among P. ficus detections was strongest at distances of ~250 m and detectable at regional scales (up to 40 km), estimates of the rate and directionality of spread were highly idiosyncratic, and P. ficus detection hotspots were spatiotemporally dynamic. Generalized linear model, boosted regression tree, and random forest modeling methods performed well in predicting habitat suitability for P. ficus. The most important predictors of P. ficus occurrence were a positive effect of precipitation in the driest month, and negative effects of elevation and distance to nearest winery. Our results indicate that 250-m quarantine and treatment zones around P. ficus detections are likely sufficient to encompass most local establishment and spread, and that implementing localized regulatory procedures may limit inadvertent P. ficus spread via anthropogenic pathways. Finally, surveys of P. ficus presence at >300 vineyard sites validated that habitat suitability estimates were significantly and positively associated with P. ficus frequency of occurrence. Our findings indicate that habitat suitability predictions may offer a robust tool for identifying areas in the study region at risk to future P. ficus invasion and prioritizing locations for early detection and preventative management efforts.

scholarly journals Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 361 ◽  
Author(s):  
Brigitte Colin ◽  
Kerrie Mengersen
Keyword(s):  
Satellite Data ◽  
Temporal Trends ◽  
Regression Tree ◽  
Spatial And Temporal Patterns ◽  
Boosted Regression Tree ◽  
Environmental Indices ◽  
Study Region ◽  
Explanatory Variables ◽  
Green Vegetation ◽  
Spatio Temporal

This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response variable in a boosted regression tree with geographic coordinates as explanatory variables. Here, a two-step approach is described in the context of a substantive case study comprising 30 years of satellite derived fractional green vegetation cover for a large region in Queensland, Australia. In addition to analysis of the entire image and timeframe, separate analyses are undertaken over decades and over sub-regions of the study region. The results demonstrate both the utility of the approach and insights into spatio-temporal trends in green vegetation for this site. These findings support the feasibility of using the proposed two-step approach and geographic coordinates in the analysis of satellite derived indices over space and time.

scholarly journals Drivers of Spatial Distributions of Basking Shark (Cetorhinus maximus) in the Southwest Pacific

2021 ◽  
Vol 8 ◽  
Author(s):  
Brittany Finucci ◽  
Clinton A. J. Duffy ◽  
Tom Brough ◽  
Malcolm P. Francis ◽  
Marco Milardi ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Regression Tree ◽  
South Pacific ◽  
Future Research ◽  
Environmental Predictors ◽  
The South ◽  
Boosted Regression Tree ◽  
Mesopelagic Zone ◽  
Basking Shark ◽  
Cetorhinus Maximus

Basking sharks (Cetorhinus maximus) were widely reported throughout New Zealand waters. Once commonly observed, and sometimes in large numbers, basking sharks are now infrequently reported. Basking shark observations are known to be highly variable across years, and their distribution and occurrence have been shown to be influenced by environmental predictors such as thermal fronts, chl-a concentration, and the abundance of prey (zooplankton). Little is known of basking sharks in the South Pacific and more information on distribution, habitat use, and migratory patterns is required to better understand the species’ regional ecology. Here, we used bootstrapped Habitat Suitability Models [HSM, ensembled from Boosted Regression Tree (BRT) and Random Forest (RF) models] to determine the drivers of basking shark distribution, predict habitat suitability and estimated uncertainty in the South Pacific for the first time. High−resolution environmental (1 km2 grid resolution) and biotic data, including inferred prey species, and all available basking shark records across New Zealand’s Exclusive Economic Zone (EEZ) were included in the ensemble HSMs. The most influential driver of modeled basking shark distribution was vertical flux of particulate organic matter at the seabed, which may indicate higher levels of primary production in the surface ocean and higher prey density in the mesopelagic zone and at the seafloor. The BRT and RF models had good predictive power (AUC and TSS > 0.7) and both models performed similarly with low variability in the model fit metrics. Areas of high basking shark habitat suitability included the east and west coasts of the South Island, Puysegur Ridge, and Auckland Island slope. The outputs produced here could be incorporated into future management framework for assessing threat and conservation needs (e.g., spatially explicit risk assessment) for this regionally protected species, as well as providing guidance for future research efforts (e.g., areas of interest for sampling).

Drivers of diversity gradients of a highly mobile marine assemblage in a mesoscale seascape

2020 ◽  
Vol 638 ◽  
pp. 149-164
Author(s):  
GM Svendsen ◽  
M Ocampo Reinaldo ◽  
MA Romero ◽  
G Williams ◽  
A Magurran ◽  
...  
Keyword(s):  
Environmental Gradients ◽  
Regression Tree ◽  
Demersal Fish ◽  
Open Ocean ◽  
Shared Resources ◽  
Thermal Front ◽  
Boosted Regression Tree ◽  
Diversity Gradients ◽  
Α Diversity ◽  
Bottom Depth

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

scholarly journals Determinants of Corporate Anti-Corruption Disclosure: The Case of the Emerging Economics

2021 ◽  
Vol 13 (6) ◽  
pp. 3462
Author(s):  
Maider Aldaz Odriozola ◽  
Igor Álvarez Etxeberria
Keyword(s):  
Empirical Studies ◽  
Geographical Area ◽  
Control Variable ◽  
Emerging Countries ◽  
Point Of View ◽  
Press Freedom ◽  
Negative Effects ◽  
Key Factor ◽  
Using Data ◽  
Positive Effect

Corruption is a key factor that affects countries’ development, with emerging countries being a geographical area in which it tends to generate greater negative effects. However, few empirical studies analyze corruption from the point of view of disclosure by companies in this relevant geographical area. Based on a regression analysis using data from the 96 large companies from 15 emerging countries included in the 2016 International Transparency Report, this paper seeks to understand what determinants affect such disclosure. In that context, this paper provides empirical evidence to understand the factors that influence reporting on anti-corruption mechanisms in an area of high economic importance that has been little studied to date, pointing to the positive effect of press freedom in a country where the company is located and with the industry being the unique control variable that strengthens this relationship.

scholarly journals Inhibition or Facilitation? Contrasted Inter-Specific Interactions in Sphagnum under Laboratory and Field Conditions

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1554
Author(s):  
Chao Liu ◽  
Zhao-Jun Bu ◽  
Azim Mallik ◽  
Yong-Da Chen ◽  
Xue-Feng Hu ◽  
...  
Keyword(s):  
Field Experiment ◽  
Species Interactions ◽  
Laboratory Experiments ◽  
Resource Competition ◽  
Specific Interactions ◽  
Interaction Type ◽  
Negative Effects ◽  
Experiment Data ◽  
The Stability ◽  
Positive Effect

In a natural environment, plants usually interact with their neighbors predominantly through resource competition, allelopathy, and facilitation. The occurrence of the positive effect of allelopathy between peat mosses (Sphagnum L.) is rare, but it has been observed in a field experiment. It is unclear whether the stability of the water table level in peat induces positive vs. negative effects of allelopathy and how that is related to phenolic allelochemical production in Sphagnum. Based on field experiment data, we established a laboratory experiment with three neighborhood treatments to measure inter-specific interactions between Sphagnum angustifolium (Russ.) C. Jens and Sphagnum magellanicum Brid. We found that the two species were strongly suppressed by the allelopathic effects of each other. S. magellanicum allelopathically facilitated S. angustifolium in the field but inhibited it in the laboratory, and relative allelopathy intensity appeared to be positively related to the content of released phenolics. We conclude that the interaction type and intensity between plants are dependent on environmental conditions. The concentration of phenolics alone may not explain the type and relative intensity of allelopathy. Carefully designed combined field and laboratory experiments are necessary to reveal the mechanism of species interactions in natural communities.

Social capital and national innovation system: a cross-country analysis

2014 ◽  
Vol 21 (4) ◽  
pp. 453-475 ◽  
Author(s):  
Sepehr Ghazinoory ◽  
Ali Bitaab ◽  
Ardeshir Lohrasbi
Keyword(s):  
Social Capital ◽  
National Level ◽  
Innovation System ◽  
Negative Effects ◽  
Content Type ◽  
Research Findings ◽  
Different Dimensions ◽  
The Impact ◽  
Positive Effect

Purpose – In the last two decades, researchers have paid much attention to the role of cultural values on economic and social development. In particular, the crucial role of different aspects of culture on the development of innovation has been stressed in the literature. Consequently, it is vital to understand how social capital, as a core cultural value, affects the innovation process and the innovative performance at the national level. However, to date, the impact of different dimensions of social capital and innovation has not been properly portrayed or explained. Thus, the purpose of this paper is to investigate the influence of four different dimensions of social capital (institutional and interpersonal, associational life and norms) on two of the main functions of national innovation system (NIS) (entrepreneurship and knowledge creation) based on over 50,000 observations in 34 countries. Design/methodology/approach – In this regard, national-level data from the World Values Survey database was employed to quantify social capital. Entrepreneurship is, in turn, assumed to consist of three sub-indexes and 14 indicators based on the Global Entrepreneurship Index. Knowledge creation is also measured through US Patent Office applications. Also, exploratory factor analysis and structural equation modeling approach were used to build the measurement model and investigate the impact that each factor of social capital had on entrepreneurship and knowledge application, respectively. Measurement and structural models were built and their reliability and validity were tested using various fit indices. Research findings suggest the strong positive effect of institutional trust and networking on entrepreneurship. Also, interpersonal trust and networks were shown to have high influence on knowledge development at the national level. Norms appear to have naïve to medium negative effects on both functions. Findings – Research findings suggest the strong positive effect of institutional trust and networking on entrepreneurship. Also, interpersonal trust and networks were shown to have high influence on knowledge development at the national level. Norms appear to have naïve to medium negative effects on both functions. Originality/value – However, to date, the impact of different dimensions of social capital and innovation has not been properly portrayed or explained.

scholarly journals Numerical Studies of the Effects of Water Capsules on Self-Healing Efficiency and Mechanical Properties in Cementitious Materials

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Haoliang Huang ◽  
Guang Ye
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Elastic Modulus ◽  
Cementitious Materials ◽  
Self Healing ◽  
Negative Effects ◽  
Numerical Studies ◽  
Healing Efficiency ◽  
The Impact ◽  
Positive Effect

In this research, self-healing due to further hydration of unhydrated cement particles is taken as an example for investigating the effects of capsules on the self-healing efficiency and mechanical properties of cementitious materials. The efficiency of supply of water by using capsules as a function of capsule dosages and sizes was determined numerically. By knowing the amount of water supplied via capsules, the efficiency of self-healing due to further hydration of unhydrated cement was quantified. In addition, the impact of capsules on mechanical properties was investigated numerically. The amount of released water increases with the dosage of capsules at different slops as the size of capsules varies. Concerning the best efficiency of self-healing, the optimizing size of capsules is 6.5 mm for capsule dosages of 3%, 5%, and 7%, respectively. Both elastic modulus and tensile strength of cementitious materials decrease with the increase of capsule. The decreasing tendency of tensile strength is larger than that of elastic modulus. However, it was found that the increase of positive effect (the capacity of inducing self-healing) of capsules is larger than that of negative effects (decreasing mechanical properties) when the dosage of capsules increases.

Working with Industrial Cobots: The Influence of Reliability and Transparency on Perception and Trust

2021 ◽  
Vol 65 (1) ◽  
pp. 77-81
Author(s):  
Lina Kluy ◽  
Eileen Roesler
Keyword(s):  
Study Design ◽  
Real Life ◽  
Error Rates ◽  
Future Research ◽  
Negative Effects ◽  
Perceived Safety ◽  
Fine Grained ◽  
Online Study ◽  
Human Robot Collaboration ◽  
Positive Effect

Industrial human-robot collaboration (HRC) is not yet widely spread but on the rise. This development raises the question about properties collaborative robots (cobots) need, to enable a pleasant and smooth interaction. Therefore, this study investigated the influence of transparency and reliability on perception of and trust towards cobots. A video-enhanced online study with 124 participants was conducted. Transparency was provided through the presentation of differing information, and reliability was manipulated through differing error rates. The results showed a positive effect of transparency on perceived safety and intelligence. Reliability had a positive effect on perceived intelligence, likeability and trust. The effect of reliability on trust was more pronounced for low transparent robots. The results indicate the relevance of carefully selected information to counteract negative effects of failures. Future research should transfer the study design into a real-life experiment with more fine-grained levels of transparency and reliability.

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