Mapping of the Asian Longhorned Beetle’s Time to Maturity and Risk to Invasion at Contiguous United States Extent
Alexander P. Kappel (Corresponding Author)Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA [email protected]
R. Talbot TrotterU.S. Forest Service, Northern Research Station, 51 Mill Pond Rd, Hamden, CT 06514
Melody A. KeenaU.S. Forest Service, Northern Research Station, 51 Mill Pond Rd, Hamden, CT 06514
John RoganGraduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610
Christopher A. WilliamsGraduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610
Abstract
Anoplophora glabripennis, the Asian Longhorned Beetle (ALB), is an invasive species of high economic and ecological relevance given the potential it has to cause tree damage, and sometimes mortality, in the United States. Because this pest is introduced by transport in wood-packing products from Asia, ongoing trade activities pose continuous risk of transport and opportunities for introduction. Therefore, a geographic understanding of the spatial distribution of risk factors associated with ALB invasions is needed. Chief among the multiple risk factors are (a) the potential for infestation based on host tree species presence/absence, and (b) the temperature regime as a determinant of ALB’s growth time to maturity. This study uses an empirical model of ALB’s time to maturity as a function of temperature, along with a model of heat transfer in the wood of the host and spatial data describing host species presence/absence data, to produce a map of risk factors across the conterminous United States to define potential for ALB infestation and relative threat of impact. Results show that the region with greatest risk of ALB infestation is the eastern half of the country, with lower risk across most of the western half due to low abundance of host species, less urban area, and prevalence of cold, high elevations. Risk is high in southeastern states primarily because of temperature, while risk is high in northeastern and northern central states because of high abundance of host species.
Keywords
Asian Longhorned Beetle, Anoplophora glabripennis, Invasion, Colonization, Risk, Maturity, United States, Modeling, Degree Days, Temperature, Host Species, Distribution, Instar
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5. Supporting Information
5.1 Supporting Figures
Figure 1- A comparison of the two risk scenarios. Green areas contain species known to be at risk to ALB given a past observed vulnerability. These green areas define the spatial extent of the species scenario. Red areas contain species that have not yet been observed as vulnerable but that may be at risk due to membership in a genus which already contains other vulnerable species. These red areas plus the green areas define the spatial extent of the genus scenario.
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Figure 2- ALB years to maturity for viable host area defined by urban areas and genus scenario risk extent.
3
5.2 Supporting Tables
ALB Susceptible Species Layer ComponentsCommon Scientific
1 boxelder Acer negundo2 black maple Acer nigrum3 Norway maple Acer platanoides4 red maple Acer rubrum5 silver maple Acer saccharinum6 sugar maple Acer saccharum7 yellow buckeye Aesculus flava8 Ohio buckeye Aesculus glabra9 buckeye, horsechestnut spp. Aesculus spp.10
mimosa, silktree Albizia julibrissin
11
red alder Alnus rubra
12
river birch Betula nigra
13
paper birch Betula papyrifera
14
gray birch Betula populifolia
15
hackberry Celtis occidentalis
16
Russian-olive Elaeagnus angustifolia
17
white ash Fraxinus americana
18
green ash Fraxinus pennsylvanica
19
honeylocust Gleditsia triacanthos
20
sweetgum Liquidambar styraciflua
21
yellow-poplar Liriodendron tulipifera
22
white mulberry Morus alba
23
eastern hophornbeam Ostrya virginiana
24
American sycamore Platanus occidentalis
25
black cottonwood Populus balsamifera
26
silver poplar Populus alba
6
27
eastern cottonwood Populus deltoides
28
quaking aspen Populus tremuloides
29
Lombardy poplar Populus nigra
30
peach Prunus persica
31
black locust Robinia pseudoacacia
32
white willow Salix alba
33
black willow Salix nigra
34
European mountain-ash Sorbus aucuparia
35
American basswood Tilia americana
36
white basswood Tilia americana
37
Carolina basswood Tilia americana
38
American elm Ulmus americana
39
Siberian elm Ulmus pumila
Table 1- List of tree species composing the species scenario risk extent. This list is made up of known susceptible species as referenced by Meng et al. (2015).
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ALB Susceptible Genus Layer ComponentsCommon Scientific Common Scientific
1 Florida maple Acer barbatum 51 sweetgum Liquidambar styraciflua2 Rocky Mountain maple Acer glabrum 52 southern crab apple Malus angustifolia3 chalk maple Acer leucoderme 53 sweet crab apple Malus coronaria4 bigleaf maple Acer macrophyllum 54 Oregon crab apple Malus fusca5 boxelder Acer negundo 55 prairie crab apple Malus ioensis6 black maple Acer nigrum 56 apple spp. Malus spp.7 striped maple Acer pensylvanicum 57 chinaberry Melia azedarach8 Norway maple Acer platanoides 58 white mulberry Morus alba9 red maple Acer rubrum 59 Texas mulberry Morus microphylla10 silver maple Acer saccharinum 60 red mulberry Morus rubra11 sugar maple Acer saccharum 61 mulberry spp. Morus spp.12 mountain maple Acer spicatum 62 eastern hophornbeam Ostrya virginiana13 maple spp. Acer spp. 63 sycamore spp. Platanus spp.14 California buckeye Aesculus californica 64 silver poplar Populus alba15 yellow buckeye Aesculus flava 65 balsam poplar Populus balsamifera16 Ohio buckeye Aesculus glabra 66 black cottonwood Populus balsamifera17 Texas buckeye Aesculus glabra 67 eastern cottonwood Populus deltoides18 buckeye, horsechestnut spp. Aesculus spp. 68 plains cottonwood Populus deltoides19 painted buckeye Aesculus sylvatica 69 bigtooth aspen Populus grandidentata20 European alder Alnus glutinosa 70 swamp cottonwood Populus heterophylla21 red alder Alnus rubra 71 Lombardy poplar Populus nigra22 alder spp. Alnus spp. 72 cottonwood and poplar spp. Populus spp.23 yellow birch Betula alleghaniensis 73 quaking aspen Populus tremuloides24 sweet birch Betula lenta 74 American plum Prunus americana25 river birch Betula nigra 75 sweet cherry, domesticated Prunus avium26 paper birch Betula papyrifera 76 Canada plum Prunus nigra27 gray birch Betula populifolia 77 pin cherry Prunus pensylvanica28 birch spp. Betula spp. 78 peach Prunus persica29 American hornbeam, musclewood Carpinus caroliniana 79 black cherry Prunus serotina30 sugarberry Celtis laevigata 80 cherry and plum spp. Prunus spp.31 netleaf hackberry Celtis laevigata 81 chokecherry Prunus virginiana32 hackberry Celtis occidentalis 82 black locust Robinia pseudoacacia33 hackberry spp. Celtis spp. 83 white willow Salix alba34 cockspur hawthorn Crataegus crus-galli 84 peachleaf willow Salix amygdaloides35 downy hawthorn Crataegus mollis 85 Bebb willow Salix bebbiana36 hawthorn spp. Crataegus spp. 86 coastal plain willow Salix caroliniana37 Russian-olive Elaeagnus angustifolia 87 black willow Salix nigra38 American beech Fagus grandifolia 88 weeping willow Salix sepulcralis39 white ash Fraxinus americana 89 willow spp. Salix spp.40 Carolina ash Fraxinus caroliniana 90 American mountain-ash Sorbus americana41 Oregon ash Fraxinus latifolia 91 European mountain-ash Sorbus aucuparia42 black ash Fraxinus nigra 92 American basswood Tilia americana43 green ash Fraxinus pennsylvanica 93 white basswood Tilia americana44 pumpkin ash Fraxinus profunda 94 Carolina basswood Tilia americana45 blue ash Fraxinus quadrangulata 95 basswood spp. Tilia spp.46 ash spp. Fraxinus spp. 96 winged elm Ulmus alata47 Texas ash Fraxinus texensis 97 American elm Ulmus americana48 waterlocust Gleditsia aquatica 98 cedar elm Ulmus crassifolia49 honeylocust spp. Gleditsia spp. 99 Siberian elm Ulmus pumila50 honeylocust Gleditsia triacanthos 100 slippery elm Ulmus rubra
101 September elm Ulmus serotina102 elm spp. Ulmus spp.103 rock elm Ulmus thomasii
Table 2- List of tree species composing the genus scenario risk extent. This list is made up of species belonging to known susceptible genera as referenced by Meng et al. (2015).
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State Summary Statistics: Genus Vulnerability ScenarioName Mea
nSTD Min Max % Area %
Timber1 District of Columbia 1.568 0.01
91.540 1.61
6100.000 42.011
2 West Virginia 2.235 0.561
1.594 4.553
98.657 50.667
3 Connecticut 2.504 0.149
1.751 3.441
98.507 51.446
4 New Hampshire 3.665 1.212
2.488 9.630
97.742 50.164
5 Massachusetts 2.697 0.353
2.395 3.773
96.138 44.903
6 Vermont 3.828 0.828
2.480 6.595
95.728 66.754
7 Alabama 1.143 0.243
0.751 1.660
95.517 23.522
8 Pennsylvania 2.605 0.477
1.589 3.726
95.306 59.385
9 Maine 4.114 0.876
2.600 9.633
95.139 41.583
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New York 3.123 0.757
1.644 7.606
94.968 65.703
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South Carolina 1.209 0.209
0.808 2.416
94.857 24.356
12
Rhode Island 2.488 0.035
2.405 2.633
94.741 38.620
13
Georgia 1.090 0.289
0.764 2.468
93.851 20.614
14
North Carolina 1.560 0.383
1.282 3.778
93.803 34.490
15
Virginia 1.774 0.370
1.386 4.518
93.664 35.606
16
Tennessee 1.561 0.212
1.321 4.389
91.936 37.282
17
New Jersey 1.909 0.346
1.589 2.677
89.763 32.681
18
Mississippi 1.087 0.227
0.770 1.482
89.343 27.292
19
Michigan 3.215 0.721
1.770 7.649
87.334 55.671
20
Kentucky 1.613 0.094
1.389 2.474
86.994 44.442
21
Delaware 1.631 0.018
1.592 1.726
85.003 43.644
22
Maryland 1.752 0.360
1.518 3.488
84.989 46.562
23
Wisconsin 3.001 0.512
2.384 4.595
81.461 56.806
24
Arkansas 1.362 0.126
0.858 1.652
80.903 20.644
25
Missouri 1.602 0.055
1.370 1.797
73.783 19.040
26
Ohio 2.147 0.340
1.627 2.658
72.660 62.073
27
Louisiana 0.822 0.060
0.712 1.252
69.279 25.450
28
Indiana 1.852 0.307
1.512 2.480
61.043 56.223
29
Oklahoma 1.348 0.090
0.866 1.704
59.610 21.283
30
Minnesota 3.414 0.721
2.438 6.622
59.071 52.436
3 Florida 0.736 0.05 0.592 0.84 58.789 9.217
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1 4 732
Washington 4.883 1.597
1.723 9.674
44.309 6.506
33
Illinois 1.769 0.294
1.471 2.504
44.110 46.924
34
Texas 0.846 0.149
0.619 1.693
39.372 12.463
35
Iowa 2.098 0.347
1.649 2.627
31.906 54.539
36
Oregon 4.329 1.475
1.778 9.800
30.774 4.590
37
Kansas 1.575 0.055
1.400 2.373
29.231 57.447
38
California 2.309 1.365
0.619 9.986
22.806 1.445
39
Idaho 5.367 1.769
1.751 9.677
21.704 2.295
40
Nebraska 2.058 0.369
1.633 2.773
17.156 49.372
41
Colorado 5.827 2.190
1.586 9.677
16.025 12.498
42
Montana 5.611 2.207
2.493 9.668
12.273 2.729
43
Utah 5.281 2.000
0.803 9.666
12.138 6.559
44
North Dakota 3.061 0.488
2.490 4.562
11.286 67.991
45
Wyoming 6.242 2.089
2.485 9.677
10.051 5.704
46
South Dakota 3.017 1.217
1.740 8.594
9.579 14.779
47
New Mexico 4.490 2.348
0.822 9.644
4.431 1.697
48
Nevada 4.651 2.155
0.627 9.663
3.626 1.315
49
Arizona 2.678 2.447
0.619 9.636
3.602 1.190
Table 3- Summary statistics for states and District of Columbia, sorted by percent area at risk, given the genus scenario. ‘% Area’ refers to the vulnerable grid cell percent of a state’s area and ‘% Timber’ refers to the vulnerable percent of a state’s timber basal area. Mean, standard deviation, min, and max, refer to time to maturity.
Summary Statistics for Top 100 Most Populated Metropolitan AreasName Mea
nStd. Dev.
Min Max
1 McAllen, TX 0.628 0.002 0.625
0.633
2 Miami, FL 0.631 0.013 0.614
0.666
3 Cape Coral, FL 0.658 0.004 0.641
0.666
4 Phoenix--Mesa, AZ 0.670 0.012 0.649
0.786
5 North Port--Port Charlotte, FL 0.671 0.003 0.668
0.677
6 Lakeland, FL 0.680 0.002 0.677
0.685
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7 Tampa--St. Petersburg, FL 0.685 0.010 0.666
0.712
8 Palm Bay--Melbourne, FL 0.689 0.003 0.682
0.696
9 Orlando, FL 0.691 0.006 0.685
0.712
10
Deltona, FL 0.713 0.010 0.699
0.729
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Houston, TX 0.728 0.010 0.710
0.770
12
New Orleans, LA 0.732 0.010 0.712
0.756
13
Tucson, AZ 0.733 0.033 0.696
1.332
14
San Antonio, TX 0.741 0.019 0.712
0.822
15
Las Vegas--Henderson, NV 0.742 0.036 0.690
1.392
16
Jacksonville, FL 0.746 0.011 0.729
0.773
17
Austin, TX 0.762 0.009 0.748
0.786
18
Baton Rouge, LA 0.783 0.005 0.773
0.800
19
Dallas--Fort Worth--Arlington, TX 0.806 0.016 0.770
0.860
20
Bakersfield, CA 0.859 0.011 0.833
0.890
21
Charleston--North Charleston, SC 0.864 0.021 0.844
0.912
22
El Paso, TX--NM 0.866 0.108 0.789
1.356
23
Jackson, MS 0.887 0.015 0.863
0.923
24
Los Angeles--Long Beach--Anaheim, CA 1.020 0.126 0.838
1.740
25
Riverside--San Bernardino, CA 1.037 0.142 0.896
1.753
26
Fresno, CA 1.144 0.118 0.937
1.280
27
San Diego, CA 1.216 0.121 0.981
1.666
28
Augusta-Richmond County, GA--SC 1.222 0.055 0.989
1.310
29
Columbia, SC 1.265 0.087 0.921
1.356
30
Birmingham, AL 1.300 0.023 1.258
1.367
31
Little Rock, AR 1.316 0.011 1.290
1.345
32
Memphis, TN--MS--AR 1.342 0.013 1.321
1.370
33
Stockton, CA 1.354 0.002 1.351
1.359
34
Oklahoma City, OK 1.364 0.009 1.342
1.384
35
Sacramento, CA 1.372 0.042 1.348
1.592
36
Atlanta, GA 1.378 0.023 1.345
1.526
3 Greenville, SC 1.380 0.008 1.36 1.488
11
7 238
Tulsa, OK 1.381 0.003 1.373
1.392
39
Virginia Beach, VA 1.398 0.011 1.386
1.507
40
Raleigh, NC 1.400 0.029 1.375
1.507
41
Charlotte, NC--SC 1.408 0.054 1.367
1.573
42
Chattanooga, TN--GA 1.423 0.064 1.381
1.647
43
Durham, NC 1.451 0.042 1.392
1.523
44
Nashville-Davidson, TN 1.492 0.047 1.395
1.556
45
Wichita, KS 1.516 0.007 1.504
1.540
46
Greensboro, NC 1.518 0.009 1.477
1.537
47
Winston-Salem, NC 1.540 0.017 1.488
1.589
48
Knoxville, TN 1.540 0.021 1.474
1.600
49
Richmond, VA 1.545 0.021 1.490
1.586
50
St. Louis, MO--IL 1.553 0.019 1.515
1.597
Table 4- Summary statistics for top 100 (by population) metropolitan areas, sorted by mean time to maturity.
Summary Statistics for Top 100 Most Populated Metropolitan Areas (Continued)Name Mean Std. Dev. Min Max
51 Louisville/Jefferson County, KY--IN 1.562 0.033 1.512 1.65252 Kansas City, MO--KS 1.580 0.016 1.545 1.63853 Baltimore, MD 1.609 0.029 1.559 1.72354 Oxnard, CA 1.610 0.112 1.422 1.97855 Washington, DC--VA--MD 1.620 0.035 1.540 1.70456 Albuquerque, NM 1.627 0.046 1.584 2.48057 Cincinnati, OH--KY--IN 1.654 0.019 1.627 1.72658 San Jose, CA 1.666 0.052 1.603 1.82259 Philadelphia, PA--NJ--DE--MD 1.690 0.088 1.589 2.40860 Omaha, NE--IA 1.700 0.008 1.688 1.73261 Dayton, OH 1.705 0.027 1.641 1.77562 Indianapolis, IN 1.713 0.024 1.666 1.82263 Columbus, OH 1.739 0.087 1.682 2.43664 Harrisburg, PA 1.770 0.134 1.693 2.41465 Des Moines, IA 1.774 0.058 1.729 2.36766 Salt Lake City--West Valley City, UT 1.881 0.442 1.707 5.50467 New York--Newark, NY--NJ--CT 1.910 0.300 1.644 2.67768 Toledo, OH--MI 2.108 0.298 1.759 2.44469 San Francisco--Oakland, CA 2.139 0.518 1.630 4.91270 Chicago, IL--IN 2.144 0.321 1.723 2.50471 Ogden--Layton, UT 2.241 0.453 1.715 4.57872 Provo--Orem, UT 2.273 0.349 1.775 5.562
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73 Bridgeport--Stamford, CT--NY 2.279 0.269 1.751 2.49074 Pittsburgh, PA 2.305 0.231 1.737 2.48275 Allentown, PA--NJ 2.327 0.189 1.844 2.48576 Cleveland, OH 2.348 0.219 1.794 2.49077 New Haven, CT 2.439 0.032 2.364 2.48878 Detroit, MI 2.442 0.055 1.844 2.49079 Akron, OH 2.447 0.014 2.408 2.48080 Providence, RI--MA 2.466 0.026 2.395 2.60381 Minneapolis--St. Paul, MN--WI 2.469 0.013 2.444 2.49382 Hartford, CT 2.471 0.055 2.395 2.67183 Springfield, MA--CT 2.474 0.040 2.395 2.69384 Youngstown, OH--PA 2.475 0.005 2.463 2.48585 Boise City, ID 2.477 0.009 2.460 2.58986 Madison, WI 2.485 0.001 2.482 2.48887 Boston, MA--NH--RI 2.488 0.032 2.427 2.84788 Albany--Schenectady, NY 2.490 0.040 2.449 2.77589 Rochester, NY 2.491 0.015 2.482 2.63090 Denver--Aurora, CO 2.493 0.062 2.411 2.79791 Milwaukee, WI 2.505 0.041 2.480 2.68292 Syracuse, NY 2.520 0.062 2.485 2.73793 Worcester, MA--CT 2.531 0.059 2.482 2.73794 Muskegon, MI 2.537 0.051 2.488 2.62795 Buffalo, NY 2.541 0.101 2.485 3.46396 Scranton, PA 2.555 0.175 2.455 3.53297 Portland, OR--WA 2.843 0.278 2.611 3.55198 Colorado Springs, CO 2.908 0.514 2.474 8.58999 Spokane, WA 2.992 0.344 2.688 3.545100
Seattle, WA 3.568 0.298 2.756 5.564
Table 4 (continued)- Summary statistics for top 100 (by population) metropolitan areas, sorted by mean time to maturity.
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