Dataset - Nagy et al., 2025 - Eye to eye with Thelazia-infected canids in Central European forests
Abstract
Dataset for the manuscript titled "Eye to eye with Thelazia-infected canids in Central European forests" written by Eszter Nagy, Rebeka Ráhel Nagy, Máté Miklós, Sándor Szekeres, Bawan Mustafa Abdalrahman, Gábor Földvári, Lajos Rózsa, Éva Fok, Tamás Sréter, Tamás Tari, Melinda Kovács, Ágnes Csivincsik, and Gábor Nagy. Abstract The oriental eyeworm (Thelazia callipaeda) has been present in Europe since the late 1980s. Its occurrence in the Carpathian Basin has been known since 2014. Despite the central position of Hungary in the radial expansion of T. callipaeda in Central and Eastern Europe, to date, no comprehensive surveillance has been conducted to determine the reservoir role of wild carnivores within the Carpathian Basin. Our study involved 180 red foxes (Vulpes vulpes), 62 golden jackals (Canis aureus), 119 European badgers (Meles meles), and 10 stone martens (Martes foina). Among the Mustelidae, we did not find any infected individuals. In the red fox, the prevalence and the mean intensity proved 12.2% (CI95% = 8-18%) and 2.64 (CI95% = 1.73-4.91), respectively; while in the golden jackal, these values were 9.7% (CI95% = 4.3-20%) and 3.0 (CI95% = 1.5-6.53), respectively. The difference in prevalence and mean intensity of infection between the two hosts proved non-significant. Our eco-epidemiological analysis revealed that humid and cool climates, the moisture-indicating beech and oak-hornbeam forests, increased the risk of infection in wild carnivores. These findings highlighted the importance of humidity in the spread of T. callipaeda in the Carpathian Basin. Keywords Thelazia callipaeda, red fox, golden jackal, forest communities, summer heat-moisture index
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REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT SEBSPREV /METHOD=ENTER SHM AGRO GRASS FOREST WETHABIT ELEVATION. Regression Notes Output Created Comments Input Active Dataset Filter Weight Split File N of Rows in Working Data File Missing Value Handling Definition of Missing Cases Used Syntax Resources Processor Time Elapsed Time Memory Required Additional Memory Required for Residual Plots 22-OCT-2025 06:51:31 DataSet1 <none> <none> <none> 52 User-defined missing values are treated as missing. Statistics are based on cases with no missing values for any variable used. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT SEBSPREV /METHOD=ENTER SHM AGRO GRASS FOREST WETHABIT ELEVATION. 00:00:00,03 00:00:00,04 6512 bytes 0 bytes Page 1
Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 ELEVATION, GRASS, AGRO, WETHABIT, SHM, FORESTb .Enter Dependent Variable: SEBSPREVa. All requested variables entered.b. Model Summary Model RR Square Adjusted R Square Std. Error of the Estimate 1 ,664a,440 ,364 ,1487791822 Predictors: (Constant), ELEVATION, GRASS, AGRO, WETHABIT, SHM, FORESTa. ANOVAa Model Sum of Squares df Mean Square FSig. 1Regression Residual Total ,766 6,128 5,770 ,000b ,974 44 ,022 1,740 50 Dependent Variable: SEBSPREVa. Predictors: (Constant), ELEVATION, GRASS, AGRO, WETHABIT, SHM, FORESTb. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients tSig. BStd. Error Beta 1(Constant) SHM AGRO GRASS FOREST WETHABIT ELEVATION 1,693 ,620 2,733 ,009 -,021 ,006 -,540 -3,406 ,001 ,506 -,242 ,292 -,267 -,829 ,412 ,122 -,376 ,395 -,166 -,952 ,346 ,418 ,030 ,281 ,041 ,105 ,917 ,082 -,057 ,818 -,019 -,069 ,945 ,172 ,000 ,001 -,100 -,607 ,547 ,464 Page 2
Coefficientsa Model Collinearity Statistics Tolerance VIF 1(Constant) SHM AGRO GRASS FOREST WETHABIT ELEVATION ,506 1,978 ,122 8,178 ,418 2,392 ,082 12,223 ,172 5,829 ,464 2,154 Dependent Variable: SEBSPREVa. Collinearity Diagnosticsa Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) SHM AGRO GRASS 1 1 2 3 4 5 6 7 5,259 1,000 ,00 ,00 ,00 ,00 ,00 ,868 2,462 ,00 ,00 ,00 ,00 ,00 ,448 3,426 ,00 ,00 ,03 ,25 ,00 ,385 3,697 ,00 ,00 ,03 ,11 ,02 ,030 13,296 ,00 ,01 ,03 ,01 ,05 ,010 23,240 ,01 ,12 ,63 ,44 ,51 ,001 81,011 ,99 ,87 ,29 ,19 ,41 Collinearity Diagnosticsa Model Dimension Variance Proportions FOREST WETHABIT ELEVATION 1 1 2 3 4 5 6 7 ,00 ,00 ,00 ,00 ,12 ,00 ,00 ,01 ,00 ,02 ,02 ,00 ,05 ,02 ,64 ,51 ,48 ,01 ,41 ,36 ,34 Dependent Variable: SEBSPREVa. REGRESSION /MISSING LISTWISE Page 3
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT SEBSPREV /METHOD=ENTER SHM GRASS FOREST WETHABIT ELEVATION. Regression Notes Output Created Comments Input Active Dataset Filter Weight Split File N of Rows in Working Data File Missing Value Handling Definition of Missing Cases Used Syntax Resources Processor Time Elapsed Time Memory Required Additional Memory Required for Residual Plots 22-OCT-2025 06:51:45 DataSet1 <none> <none> <none> 52 User-defined missing values are treated as missing. Statistics are based on cases with no missing values for any variable used. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT SEBSPREV /METHOD=ENTER SHM GRASS FOREST WETHABIT ELEVATION. 00:00:00,02 00:00:00,04 5808 bytes 0 bytes Page 4
Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 ELEVATION, GRASS, FOREST, SHM, WETHABITb .Enter Dependent Variable: SEBSPREVa. All requested variables entered.b. Model Summary Model RR Square Adjusted R Square Std. Error of the Estimate 1 ,657a,432 ,368 ,1482604062 Predictors: (Constant), ELEVATION, GRASS, FOREST, SHM, WETHABITa. ANOVAa Model Sum of Squares df Mean Square FSig. 1Regression Residual Total ,751 5,150 6,834 ,000b ,989 45 ,022 1,740 50 Dependent Variable: SEBSPREVa. Predictors: (Constant), ELEVATION, GRASS, FOREST, SHM, WETHABITb. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients tSig. BStd. Error Beta 1(Constant) SHM GRASS FOREST WETHABIT ELEVATION 1,383 ,492 2,810 ,007 -,020 ,006 -,508 -3,315 ,002 ,538 -,131 ,261 -,058 -,503 ,618 ,948 ,245 ,107 ,344 2,299 ,026 ,565 ,497 ,471 ,165 1,055 ,297 ,514 ,000 ,001 -,088 -,534 ,596 ,468 Page 5
Coefficientsa Model Collinearity Statistics Tolerance VIF 1(Constant) SHM GRASS FOREST WETHABIT ELEVATION ,538 1,860 ,948 1,055 ,565 1,769 ,514 1,945 ,468 2,135 Dependent Variable: SEBSPREVa. Collinearity Diagnosticsa Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) SHM GRASS FOREST 1 1 2 3 4 5 6 4,583 1,000 ,00 ,00 ,01 ,01 ,00 ,863 2,304 ,00 ,00 ,00 ,02 ,38 ,415 3,323 ,00 ,00 ,80 ,05 ,00 ,111 6,416 ,00 ,00 ,15 ,72 ,23 ,027 13,031 ,01 ,03 ,02 ,01 ,28 ,001 64,771 ,99 ,97 ,00 ,20 ,10 Collinearity Diagnosticsa Model Dimension Variance Proportions WETHABIT ELEVATION 1 1 2 3 4 5 6 ,00 ,00 ,38 ,00 ,00 ,00 ,23 ,03 ,28 ,57 ,10 ,40 Dependent Variable: SEBSPREVa. BOOTSTRAP /SAMPLING METHOD=SIMPLE /VARIABLES TARGET=SEBSPREV INPUT= SHM GRASS FOREST WETHABIT ELEVATION /CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=1000 /MISSING USERMISSING=EXCLUDE. Page 6
Bootstrap Notes Output Created Comments Input Active Dataset Filter Weight Split File N of Rows in Working Data File Syntax Resources Processor Time Elapsed Time 22-OCT-2025 06:52:18 DataSet1 <none> <none> <none> 52 BOOTSTRAP /SAMPLING METHOD=SIMPLE /VARIABLES TARGET=SEBSPREV INPUT= SHM GRASS FOREST WETHABIT ELEVATION /CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=1000 /MISSING USERMISSING=EXCLUD E. 00:00:00,02 00:00:00,02 Bootstrap Specifications Sampling Method Number of Samples Confidence Interval Level Confidence Interval Type Simple 1000 95,0% Percentile * Generalized Linear Models. GENLIN SEBSPREV WITH SHM GRASS FOREST WETHABIT ELEVATION /MODEL SHM ELEVATION FOREST GRASS WETHABIT INTERCEPT=YES DISTRIBUTION=NORMAL LINK=IDENTITY /CRITERIA SCALE=MLE COVB=MODEL PCONVERGE=1E-006(ABSOLUTE) SINGULAR=1E-012 ANALYSISTYPE=3(WALD) CILEVEL=95 CITYPE=WALD LIKELIHOOD=FULL /MISSING CLASSMISSING=EXCLUDE /PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION (EXPONENTIATED). Generalized Linear Models Page 7
Notes Output Created Comments Input Active Dataset Filter Weight Split File N of Rows in Working Data File Missing Value Handling Definition of Missing Cases Used Weight Handling Syntax 22-OCT-2025 06:52:18 DataSet1 <none> <none> <none> 32537 User-defined missing values for factor, subject and within-subject variables are treated as missing. Statistics are based on cases with valid data for all variables in the model. Weight values are rounded to the nearest whole numbers and used as frequency weights. Cases with frequency weights that are negative, zero or missing are excluded from the analysis. GENLIN SEBSPREV WITH SHM GRASS FOREST WETHABIT ELEVATION /MODEL SHM ELEVATION FOREST GRASS WETHABIT INTERCEPT=YES DISTRIBUTION=NORMA L LINK=IDENTITY /CRITERIA SCALE=MLE COVB=MODEL PCONVERGE=1E-006 (ABSOLUTE) SINGULAR=1E-012 ANALYSISTYPE=3 (WALD) CILEVEL=95 CITYPE=WALD LIKELIHOOD=FULL /MISSING CLASSMISSING=EXCLU DE /PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION ... 00:00:05,69 Page 8
Notes Resources Processor Time Elapsed Time 00:00:05,69 00:00:04,76 Model Information Dependent Variable Probability Distribution Link Function SEBSPREV Normal Identity Case Processing Summary NPercent Unweighted N Included Excluded Total 51 100,0% 51 00,0% 0 51 100,0% 51 Continuous Variable Information NMinimum Maximum Mean Dependent Variable SEBSPREV Covariate SHM GRASS FOREST WETHABIT ELEVATION 51 ,0000000000 ,7500000000 ,1446115729 ,1865636317 51 55,66502463 72,27414330 64,00708320 4,715263592 51 ,0090103963 ,3082104049 ,0925871583 ,0823522577 51 ,0278270014 ,9702521067 ,4503684560 ,2616065292 51 ,0000000000 ,3313572339 ,0356255684 ,0620693146 51 104 305 195,63 47,262 Continuous Variable Information Std. Deviation Unweighted N Dependent Variable SEBSPREV Covariate SHM GRASS FOREST WETHABIT ELEVATION ,1865636317 51 4,715263592 51 ,0823522577 51 ,2616065292 51 ,0620693146 51 47,262 51 Page 9