scholarly journals Validating Species Distribution Models With Standardized Surveys for Ixodid Ticks in Mainland Florida

2021 ◽  
Author(s):  
Gregory E Glass ◽  
Claudia Ganser ◽  
William H Kessler
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Ixodes Scapularis ◽  
External Validation ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
The State ◽  
Ixodid Ticks ◽  
Valid Species ◽  
Distribution Models

Abstract Tick-borne pathogens are of growing concern. The U.S. Centers for Disease Control and Prevention (CDC) developed guidelines standardizing surveys of tick vectors to better monitor the changes in their occurrences. Unbiased surveillance data, from standardized surveys, are presumed critical to generate valid species distribution models (SDMs). We tested previously generated SDMs from standardized protocols for three medically important ticks [Amblyomma americanum (Linnaeus, Ixodida, Ixodidae), Ixodes scapularis (Say, Ixodida, Ixodidae), and Dermacentor variabilis (Say, Ixodida, Ixodidae)]. These previous models ruled out a quarter to half of the state as having these species, with consensus occurrence in about a quarter of the state. New surveys performed throughout 2019 on 250 transects at 43 sites indicated the rule-out functions were 100% accurate for I. scapularis and D. variabilis and 91.9% for A. americanum. As SDM concordance increased, the proportion of transects yielding ticks increased. Independent surveys of SDMs provide external validation—an aspect missing from many SDM studies.

scholarly journals Standardized Ixodid Tick Survey in Mainland Florida

Insects ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 235 ◽  
Author(s):  
Glass ◽  
Ganser ◽  
Wisely ◽  
Kessler
Keyword(s):  
Tick Species ◽  
Ixodes Scapularis ◽  
Survey Methods ◽  
Ixodid Tick ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
The State ◽  
Environmental Data ◽  
Ixodid Ticks ◽  
Tick Survey

A statewide survey of questing ixodid ticks in mainland Florida was developed consistent with U.S. CDC standards to maximize the amount of epidemiologic and environmental data gathered. Survey sites were stratified by climatic zones and proportional to recognized land cover categories. A total of 560 transects on 41 sites within the state were sampled repeatedly by flagging between 2015 and 2018. Four tick species were collected; Amblyomma americanum, Amblyomma maculatum, Ixodes scapularis and Dermacentor variabilis. All species were more commonly found in northern and central regions of the state than in southern and western regions. Adult I. scapularis were active from autumn through spring and complementary to adult A. americanum and D. variabilis. Standardized survey methods help reduce sampling biases and better characterize risk from the species surveyed. However, differences in the attractiveness of collection methods for different tick species makes cross-species comparisons a continuing challenge.

scholarly journals Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0238126
Author(s):  
Andreea M. Slatculescu ◽  
Katie M. Clow ◽  
Roman McKay ◽  
Benoit Talbot ◽  
James J. Logan ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Borrelia Burgdorferi ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Ixodes Scapularis ◽  
Distribution Models ◽  
Blacklegged Tick

scholarly journals How citizen science could improve Species Distribution Models and their independent assessment

2020 ◽  
Author(s):  
Matutini Florence ◽  
Baudry Jacques ◽  
Pain Guillaume ◽  
Sineau Morgane ◽  
Pithon Joséphine
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Cross Validation ◽  
Species Distribution Models ◽  
External Validation ◽  
Receiver Operating Curve ◽  
Sampling Effort ◽  
Independent Data ◽  
External Evaluation ◽  
Distribution Models

AbstractSpecies distribution models (SDM) have been increasingly developed in recent years but their validity is questioned. Their assessment can be improved by the use of independent data but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability. We used opportunistic presence-only data along with presence-absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross-validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent data sets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork. Cross-validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data was strongly filtered. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer’s participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

THE OCCURRENCE OF SPIROCHETES IN IXODID TICKS IN NORTH CAROLINA

1989 ◽  
Vol 24 (4) ◽  
pp. 594-602 ◽  
Author(s):  
Jay F. Levine ◽  
Charles S. Apperson ◽  
William L. Nicholson
Keyword(s):  
North Carolina ◽  
Lyme Disease ◽  
Ixodes Scapularis ◽  
Disease Patient ◽  
Antibody Test ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Ixodid Ticks ◽  
Antibody Testing ◽  
Fluorescence Antibody

Ticks were collected at 6 sites in North Carolina identified as the location of tick contact by Lyme disease patients, and at 6 sites located in counties where cases had been diagnosed. Specimens were screened for evidence of spirochete infection; fewer than 1% of the specimens collected harbored spirochetes. Indirect fluorescence antibody testing, with a species-specific monoclonal antibody, confirmed that one Ixodes scapularis Say collected at the residence of a Lyme disease patient was infected with Borrelia burgdorferi Johnson, Hyde, Schmid, and Brenner. Two specimens (Amblyomma americanum (L.) and I. scapularis) screened by a direct fluorescence antibody test with polyclonal antisera were infected with Borrelia. Spirochetes other than B. burgdorferi were found in A. americanum. No spirochetes were observed in Dermacentor variabilis (Say) or I. brunneus (Koch).

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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