Was Your State a Slacker? - 2024 USA Observations Per Capita (Edit: Canadian data and European/African maps now in comments!)

General
81

Well, seems that Portugal, were i live, is not a slacker.

4 Likes
82

I got curious with the french map - so made average observation number per observers, and then overlaid the regional and national parks to see if there’s any correlation. There doesn’t seem to be any :thinking:

6Dv1L-2024-average-observations-per-observer
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6Dv1L-2024-average-observations-per-observer_Parks
6Dv1L-2024-average-observations-per-observer_Parks1920×1075 148 KB

Data
Column 1 Column 2 Column 3 Column 4 E F
Departments Observations Population Observation Per Capita Observers Observation Per Observer
Ain 13,389 679,498 0.0197 710 18.9
Aisne 1,465 521,632 0.0028 203 7.2
Allier 1,839 332,708 0.0055 250 7.4
Alpes-de-Haute-Provence 19,530 168,161 0.1161 834 23.4
Alpes-Maritimes 47,345 1,119,571 0.0423 1,413 33.5
Ardennes 2,574 265,737 0.0097 175 14.7
Ardèche 15,156 336,501 0.0450 753 20.1
Ariège 9,980 155,813 0.0641 569 17.5
Aube 4,516 312,730 0.0144 217 20.8
Aude 15,777 378,775 0.0417 888 17.8
Aveyron 12,123 279,470 0.0434 593 20.4
Bas-Rhin 8,995 1,170,551 0.0077 696 12.9
Bouches-du-RhĂ´ne 68,300 2,078,397 0.0329 2,357 29.0
Calvados 8,307 706,605 0.0118 665 12.5
Cantal 3,480 143,567 0.0242 258 13.5
Charente 2,815 349,856 0.0080 310 9.1
Charente-Maritime 11,758 674,439 0.0174 902 13.0
Cher 1,478 295,729 0.0050 207 7.1
Corrèze 2,913 238,962 0.0122 322 9.0
Corse-du-Sud 8,822 167,658 0.0526 531 16.6
Creuse 1,346 113,922 0.0118 192 7.0
CĂ´te-dOr 9,639 537,752 0.0179 525 18.4
CĂ´tes-dArmor 26,315 611,351 0.0430 791 33.3
Deux-Sèvres 9,591 373,682 0.0257 290 33.1
Dordogne 9,797 413,192 0.0237 714 13.7
Doubs 5,467 552,321 0.0099 368 14.9
DrĂ´me 14,008 524,109 0.0267 879 15.9
Essonne 13,450 1,331,827 0.0101 732 18.4
Eure 4,766 598,339 0.0080 466 10.2
Eure-et-Loir 1,743 430,422 0.0040 212 8.2
Finistère 27,374 931,604 0.0294 1112 24.6
Gard 37,390 766,765 0.0488 1602 23.3
Gers 6,789 193,695 0.0350 340 20.0
Gironde 18,376 1,707,780 0.0108 1298 14.2
Guadeloupe 10,025 378,561 0.0265 303 33.1
Guyane 55,034 295,385 0.1863 378 145.6
Haut-Rhin 24,566 769,047 0.0319 716 34.3
Haute-Corse 23,414 187,870 0.1246 524 44.7
Haute-Garonne 17,741 1,487,804 0.0119 1,134 15.6
Haute-Loire 3,017 226,900 0.0133 294 10.3
Haute-Marne 3,074 168,200 0.0183 177 17.4
Haute-Savoie 21,652 866,490 0.0250 1273 17.0
Haute-SaĂ´ne 1,613 232,523 0.0069 170 9.5
Haute-Vienne 3,669 370,339 0.0099 310 11.8
Hautes-Alpes 27,540 141,661 0.1944 900 30.6
Hautes-PyrĂŠnĂŠes 7,221 232,534 0.0311 557 13.0
Hauts-de-Seine 5,270 1,651,407 0.0032 712 7.4
HĂŠrault 93,371 1,243,225 0.0751 2410 38.7
Ille-et-Vilaine 22,230 1,127,720 0.0197 873 25.5
Indre 5,070 213,871 0.0237 269 18.8
Indre-et-Loire 3,057 616,751 0.0050 503 6.1
Isère 25,621 1,307,146 0.0196 1267 20.2
Jura 6,095 257,483 0.0237 395 15.4
La RĂŠunion 8,861 885,700 0.0100 315 28.1
Landes 8,258 434,933 0.0190 602 13.7
Loir-et-Cher 2,539 326,941 0.0078 362 7.0
Loire 7,913 775,102 0.0102 413 19.2
Loire-Atlantique 12,247 1,503,876 0.0081 279 43.9
Loiret 5,585 689,581 0.0081 400 14.0
Lot 5,735 175,800 0.0326 496 11.6
Lot-et-Garonne 2,843 330,385 0.0086 289 9.8
Lozère 12,750 76,647 0.1663 660 19.3
Maine-et-Loire 7,880 834,135 0.0094 564 14.0
Manche 8,780 494,200 0.0178 674 13.0
Marne 5,152 562,874 0.0092 314 16.4
Martinique 6,119 349,925 0.0175 223 27.4
Mayenne 3,482 304,981 0.0114 223 15.6
Mayotte 930 320,901 0.0029 223 4.2
Meurthe-et-Moselle 4,544 730,320 0.0062 329 13.8
Meuse 5,196 178,562 0.0291 178 29.2
Morbihan 13,574 782,348 0.0174 964 14.1
Moselle 4,315 1,055,259 0.0041 498 8.7
Nièvre 2,219 198,936 0.0112 226 9.8
Nord 13,522 2,614,334 0.0052 871 15.5
Oise 4,118 830,176 0.0050 368 11.2
Orne 9,527 272,379 0.0350 330 28.9
Paris 23,534 2,087,577 0.0113 2134 11.0
Pas-de-Calais 8,689 1,455,555 0.0060 615 14.1
Puy-de-DĂ´me 7,507 665,094 0.0113 622 12.1
PyrĂŠnĂŠes-Atlantiques 11,604 706,361 0.0164 938 12.4
PyrĂŠnĂŠes-Orientales 21,700 497,810 0.0436 997 21.8
RhĂ´ne 19,305 1,926,989 0.0100 1167 16.5
Sarthe 3,640 566,096 0.0064 343 10.6
Savoie 39,590 451,819 0.0876 1,104 35.9
SaĂ´ne-et-Loire 4,124 546,695 0.0075 523 7.9
Seine-et-Marne 27,943 1,464,783 0.0191 1,120 24.9
Seine-Maritime 17,152 1,255,554 0.0137 728 23.6
Seine-Saint-Denis 4,810 1,701,072 0.0028 367 13.1
Somme 6,982 562,126 0.0124 388 18.0
Tarn 4,512 398,772 0.0113 469 9.6
Tarn-et-Garonne 2,386 267,619 0.0089 258 9.2
Territoire de Belfort 800 137,235 0.0058 66 12.1
Val-d’Oise 7,268 1,275,704 0.0057 381 19.1
Val-de-Marne 6,878 1,433,927 0.0048 498 13.8
Var 40,395 1,121,506 0.0360 1,639 24.6
Vaucluse 16,000 568,715 0.0281 991 16.1
VendĂŠe 8,810 717,301 0.0123 757 11.6
Vienne 5,046 440,921 0.0114 348 14.5
Vosges 3,923 355,431 0.0110 393 10.0
Yonne 7,340 328,774 0.0223 286 25.7
Yvelines 11,805 1,473,664 0.0080 923 12.8
4 Likes
83

NO-ooooo! There are already 15 thousand pages of Needs ID plant observations in Massachusetts!
Just kidding, go ahead … your observations are always identifiable, and your legions of followers are always on top of it. Plus, of course, your IDs far outweigh your Observations.

So which of you number crunchers is going to give us the stats on which states/countries have the highest percentages of identifications/identifiers?

3 Likes
84

As an Arkansan, I’m just happy we overtook Missouri.

2 Likes
85

Oh, thank you! That’s so kind of you to say! I will say that making IDs taught me how to make my own observations easier to ID. And, really, I just photograph organisms I already know or suspect iNat can ID for me (moths, galls). Oh, wait, even that’s not really true since I started getting into bryophytes in a serious way.

3 Likes
86

Oh, indeed, I clicked the swap symbol when editing the color gradient - which swaps the colors but not the numbers in the legend… And Salzburg had the third least, since Wien turned into the header column somehow - but with those two things in mind I think the map at least matches the numbers.

1 Like
87

In case you haven’t seen it, check out the iNat observations map for April 8th 2023 vs 2024.

4 Likes
88

Isn’t World’s End also in Sullivan? There’s a lot of forest–beautiful county and under appreciated.

89

Last map for me - Germany with a bit more detailing. It seems to me that my long-held plan to hike the Harzer Wandernadel would possibly have a significant impact on that map so I’m going to set that in motion :grin:

zLjVL-2024-germany-observation-per-capita
zLjVL-2024-germany-observation-per-capita1240×1590 140 KB

available here: https://www.datawrapper.de/_/zLjVL/

Numbers from all the offices of statistic around the land, I’ve been chasing them all day and didn’t keep the links

Data
Column 1 Column 2 Column 3 Column 4
NUTS2 statistic division (Regierungsbezirke or equivalent) 2024 Verifiable Observations Population Observation per Capita
Arnsberg 22,153 3,602,426 0.0061
Berlin 74,793 3,662,381 0.0204
Brandenburg 66,956 2,554,464 0.0262
Braunschweig 15,258 1,590,820 0.0096
Bremen 12,592 702,655 0.0179
Chemnitz 16,586 1,402,923 0.0118
Darmstadt 96,353 4,088,107 0.0236
Detmold 8,724 2,086,513 0.0042
Dresden 24,652 1,595,005 0.0155
DĂźsseldorf 23,954 5,281,469 0.0045
Freiburg 43,378 2,337,563 0.0186
Gießen 27,593 1,067,354 0.0259
Hamburg 15,097 1,851,596 0.0082
Hannover 24,975 2,121,989 0.0118
Karlsruhe 36,624 2,859,693 0.0128
Kassel 14,081 1,235,899 0.0114
Koblenz 22,062 1,526,980 0.0144
KĂśln 44,521 4,543,769 0.0098
Leipzig 28,458 1,081,978 0.0263
LĂźneburg 25,884 1,721,218 0.0150
Mecklenburg-Vorpommern 34,577 1,578,041 0.0219
Mittelfranken 16,689 1,813,946 0.0092
MĂźnster 13,968 2,679,622 0.0052
Niederbayern 19,932 1,280,685 0.0156
Oberbayern 109,170 4,820,938 0.0226
Oberfranken 11,913 1,077,349 0.0111
Oberpfalz 12,544 1,141,561 0.0110
Rheinhessen-Pfalz 33,993 2,097,257 0.0162
Saarland 14,321 1,014,047 0.0141
Sachsen-Anhalt 32,514 2,144,570 0.0152
Schleswig-Holstein 123,457 2,953,202 0.0418
Schwaben 33,509 1,962,086 0.0171
Stuttgart 40,232 4,226,394 0.0095
ThĂźringen 25,629 2,114,870 0.0121
Trier 12,375 548,256 0.0226
TĂźbingen 26,513 1,915,610 0.0138
Unterfranken 18,524 1,338,497 0.0138
Weser-Ems 22,357 2,469,749 0.0091
4 Likes
90

I thought North Carolina might be interesting; I was not disappointed! More observations in the mountains, along the coast, and in tech/urban areas.

Like my Pennsylvania map, this one does all-time verifiable observations:

Screenshot 2025-01-16 6.10.01 PM
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Here’s the link where you can hover over each county:
https://www.datawrapper.de/_/tHY55/

As a table (listing all states)
County Observations Population Observations per Capita
Alamance 46,305 181,097 0.26
Alexander 9564 36,231 0.26
Alleghany 7,591 11,513 0.66
Anson 17,888 21,619 0.83
Ashe 28,240 26,694 1.06
Avery 50,402 17,510 2.88
Beaufort 10977 44,003 0.25
Bertie 1735 16,856 0.10
Bladen 9,767 29,153 0.34
Brunswick 58,533 160,440 0.36
Buncombe 253,020 277,047 0.91
Burke 26022 89,974 0.29
Cabarrus 45,068 242,880 0.19
Caldwell 23,026 81,960 0.28
Camden 2370 10,737 0.22
Carteret 46,335 70,268 0.66
Caswell 3810 22,461 0.17
Catawba 34,586 166,196 0.21
Chatham 89,845 81,248 1.11
Cherokee 7,619 29,691 0.26
Chowan 2040 13,710 0.15
Clay 14,334 11,725 1.22
Cleveland 3874 100,498 0.04
Columbus 9625 50,389 0.19
Craven 14818 103,605 0.14
Cumberland 24953 337,970 0.07
Currituck 23072 31,396 0.73
Dare 94578 38,019 2.49
Davidson 21913 176,388 0.12
Davie 4619 44,249 0.10
Duplin 2006 49,178 0.04
Durham 211837 337,263 0.63
Edgecombe 2087 48,491 0.04
Forsyth 46014 393,062 0.12
Franklin 9197 77,561 0.12
Gaston 25912 240,820 0.11
Gates 5339 10,297 0.52
Graham 14389 7,985 1.80
Granville 11326 62,174 0.18
Greene 693 20,153 0.03
Guilford 95,683 550,202 0.17
Halifax 4936 46,616 0.11
Harnett 17954 140,984 0.13
Haywood 70005 63,949 1.09
Henderson 48976 120,597 0.41
Hertford 1183 18,772 0.06
Hoke 6435 55,054 0.12
Hyde 15739 4,671 3.37
Iredell 20032 202,038 0.10
Jackson 56935 44,274 1.29
Johnston 21734 241,049 0.09
Jones 4536 9,208 0.49
Lee 5944 67,308 0.09
Lenoir 3266 53,966 0.06
Lincoln 4655 94,819 0.05
McDowell 15601 44,521 0.35
Macon 50588 38,152 1.33
Madison 34311 21,753 1.58
Martin 2243 21,183 0.11
Mecklenburg 132432 1,162,168 0.11
Mitchell 14666 14,721 1.00
Montgomery 10350 25,833 0.40
Moore 22641 107,861 0.21
Nash 9268 97,802 0.09
New Hanover 92278 239,514 0.39
Northampton 963 16,503 0.06
Onslow 20633 213,447 0.10
Orange 276645 150,913 1.83
Pamlico 2075 12,521 0.17
Pasquotank 1942 41,417 0.05
Pender 21340 67,464 0.32
Perquimans 1448 13,278 0.11
Person 7508 39,461 0.19
Pitt 13998 174,842 0.08
Polk 15115 19,742 0.77
Randolph 27564 146,496 0.19
Richmond 10574 42,068 0.25
Robeson 12318 116,438 0.11
Rockingham 10077 92,416 0.11
Rowan 12932 152,450 0.08
Rutherford 15430 64,692 0.24
Sampson 2976 59,514 0.05
Scotland 14576 33,567 0.43
Stanly 10908 64,999 0.17
Stokes 14598 45,493 0.32
Surry 12717 71,774 0.18
Swain 68609 13,827 4.96
Transylvania 68281 33,193 2.06
Tyrrell 5668 3,480 1.63
Union 34664 257,682 0.13
Vance 3180 41,263 0.08
Wake 460863 1,194,900 0.39
Warren 2413 18,615 0.13
Washington 3772 10,548 0.36
Watauga 80910 54,972 1.47
Wayne 6150 117,748 0.05
Wilkes 15296 65,987 0.23
Wilson 4112 78,792 0.05
Yadkin 3029 37,722 0.08
Yancey 34246 18,524 1.85
1 Like
91

Interested in Michigan by counties, I think my state’s top observer is also in my county for the most part, so that would be cool to see. It’s neat, statistics wise, how my state is doing well for Average Observations per Observer, but not so great in Observations per Capita. It reminds me of how data can be used to show a number of different things different ways, and how this property can be manipulated.

4 Likes
92

So I got curious again, about the average number of ID per identifier, the average number of observations, etc all in Europe, and next I overlaid the participating cities for the City Nature Challenge.

mpbbj-2024-average-number-of-observation-per-observer
mpbbj-2024-average-number-of-observation-per-observer1240×1314 124 KB

Clickable map: https://www.datawrapper.de/_/mpbbj/
Pf7Dp-2024-average-number-of-id-per-identifier
Pf7Dp-2024-average-number-of-id-per-identifier1240×1326 125 KB

Clickable map: https://www.datawrapper.de/_/Pf7Dp/

UhSGu-2024-average-number-of-species-observed-per-observer
UhSGu-2024-average-number-of-species-observed-per-observer1240×1300 124 KB

clickable map: https://www.datawrapper.de/_/UhSGu/

Maps + CNC cities

2024 map plus CNC
2024 map plus CNC1920×677 76.7 KB

CNC data: https://www.citynaturechallenge.org/past-results

all data
Column 1 Column 2 Column 3 Column 4 E F G H I J K
Country 2024 Verifiable Observation 2024 Population 2024 Observers 2024 Identifiers 2024 Species Obs. per Capita Observer percentage in population 2024 Average Obs By Observer 2024 Average Number Species Observed 2024 Average ID by Identifier
ALB 18,668 2,761,785 557 1,448 3,780 0.0068 0.02% 33.52 6.79 12.89
AND 2,258 85,101 151 441 879 0.0265 0.18% 14.95 5.82 5.12
AUT 678,760 9,158,750 10,702 6,520 14,834 0.0741 0.12% 63.42 1.39 104.10
BEL 121,154 11,763,650 4,822 3,660 8,304 0.0103 0.04% 25.13 1.72 33.10
BGR 37,486 6,445,481 881 1,923 5,368 0.0058 0.01% 42.55 6.09 19.49
BIH 12,545 3,417,089 274 904 2,483 0.0037 0.01% 45.78 9.06 13.88
BLR 52,439 9,408,350 925 2,015 5,493 0.0056 0.01% 56.69 5.94 26.02
CHE 191,282 8,960,800 6,228 4,404 10,809 0.0213 0.07% 30.71 1.74 43.43
CYP 19,930 933,505 607 1,309 2,600 0.0213 0.07% 32.83 4.28 15.23
CZE 304,370 10,900,555 10,335 5,006 10,043 0.0279 0.09% 29.45 0.97 60.80
DEU 1,294,886 83,445,000 26,979 10,582 19,203 0.0155 0.03% 48.00 0.71 122.37
DNK 289,101 5,961,249 15,911 4,640 9,603 0.0485 0.27% 18.17 0.60 62.31
ESP 1,164,353 48,610,458 24,306 9,981 23,009 0.0240 0.05% 47.90 0.95 116.66
EST 15,662 1,374,687 699 1,241 3,161 0.0114 0.05% 22.41 4.52 12.62
FIN 286,265 5,603,851 8,203 4,367 8,777 0.0511 0.15% 34.90 1.07 65.55
FRA 1,191,868 68,401,997 33,699 10,434 23,768 0.0174 0.05% 35.37 0.71 114.23
GBR 1,659,877 67,025,542 44,657 11,586 15,877 0.0248 0.07% 37.17 0.36 143.27
GRC 127,918 10,397,193 5,387 3,840 9,438 0.0123 0.05% 23.75 1.75 33.31
HRV 99,978 3,861,967 3,533 3,276 8,381 0.0259 0.09% 28.30 2.37 30.52
HUN 168,450 9,548,627 2,485 3,392 10,210 0.0176 0.03% 67.79 4.11 49.66
IRL 71,672 5,343,805 3,501 2,714 5,199 0.0134 0.07% 20.47 1.49 26.41
ISL 29,100 398,940 1,471 1,471 1,504 0.0729 0.37% 19.78 1.02 19.78
ITA 773,092 58,989,749 24,800 9,066 18,117 0.0131 0.04% 31.17 0.73 85.27
KOS 1,005 1,773,971 78 389 657 0.0006 0.00% 12.88 8.42 2.58
LIE 1,265 40,023 103 340 661 0.0316 0.26% 12.28 6.42 3.72
LTU 71,884 2,885,891 2,021 2,461 6,381 0.0249 0.07% 35.57 3.16 29.21
LUX 79,723 672,050 1,752 2,681 5,236 0.1186 0.26% 45.50 2.99 29.74
LVA 18,262 1,871,882 519 1,416 3,846 0.0098 0.03% 35.19 7.41 12.90
MCO 859 38,300 129 215 340 0.0224 0.34% 6.66 2.64 4.00
MDA 3,249 2,423,287 105 607 1,307 0.0013 0.00% 30.94 12.45 5.35
MKD 4,623 1,826,247 250 659 1,758 0.0025 0.01% 18.49 7.03 7.02
MLT 7,273 563,443 462 814 1,176 0.0129 0.08% 15.74 2.55 8.93
MNE 18,551 616,695 632 1,424 3,775 0.0301 0.10% 29.35 5.97 13.03
NLD 205,901 17,942,942 7,704 4,576 9,446 0.0115 0.04% 26.73 1.23 45.00
NOR 74,575 5,550,203 3,254 2,674 5,195 0.0134 0.06% 22.92 1.60 27.89
POL 370,057 36,620,970 5,855 5,136 11,399 0.0101 0.02% 63.20 1.95 72.05
PRT 489,746 10,639,726 11,513 6,511 13,133 0.0460 0.11% 42.54 1.14 75.22
ROU 68,230 19,064,409 1,707 2,876 7,067 0.0036 0.01% 39.97 4.14 23.72
SMR 538 33,812 51 178 298 0.0159 0.15% 10.55 5.84 3.02
SRB 35,511 6,605,168 797 2,052 4,685 0.0054 0.01% 44.56 5.88 17.31
SVK 69,904 5,424,687 1,711 2,589 6,876 0.0129 0.03% 40.86 4.02 27.00
SVN 53,710 2,123,949 1,958 2,361 6,077 0.0253 0.09% 27.43 3.10 22.75
SWE 153,267 10,551,707 5,688 3,794 9,007 0.0145 0.05% 26.95 1.58 40.40
TUR 86,983 85,372,377 3,377 3,369 8,734 0.0010 0.00% 25.76 2.59 25.82
UKR 302,965 40,997,698 3,110 4,580 10,914 0.0074 0.01% 97.42 3.51 66.15
5 Likes
93

Indeed! Pitt County, despite (as I mentioned earlier) appearing well-covered in terms of the number of pins, is near the low end of the scale in observations per capita. I think it is important to note that those three dark green coastal counties are sparsely populated, so it doesn’t take as many total observations to be higher per capita. I’ve a hunch that the same is true of the mountains.

On the other hand, that “island” of green in the middle of the state is the Triangle Area – Raleigh, Durham, Chapel Hill – which is a densly populated urban zone; so we can conclude that interest in iNaturalist is also quite high there. In contrast, North Carolina’s other major city, Charlotte, appears as a medium pink (the county with the little “tab” sticking out into South Carolina).

3 Likes
94

I’m just not finding the whole ‘slacker’ and I suppose ‘performer’ paradigmn a comfortable fit for how I see iNat, anyhow.

Aside from the fact that we all know that the quantity vs quality is a weak way to measure the scientific with of observations or even biodiversity and demographic distribution of participation, does it recall anything useful about how well citizen science and even deeper, cultural connection with nature exists within states?

To me it comes down to how well does a place’s population demonstrate a respect and connection to the natural world that goes way beyond reserves and parks or participation in measures to protect and support ‘poster’ (usually ‘cute’) species.

How well do they pass on and nurture a positive and understanding of the environments they contain? Who leads that list in the developed world? What can we learn from their efforts to make it work better?

Please pardon my rather clumsy explanation here. I’ve just returned home after a week in hospital with serious pneumonia and my brain is still pretty weak and unfocused. [Oh, bacteria, you sly masters of the living world.]

11 Likes
95

I hope you recover quickly. :pray:

9 Likes
96

I 100% agree those are deeper, more important issues than the number of iNaturalist observations per capita!

Still, I think it’s neat to see - an interesting, although (very) imperfect measure of public engagement with iNaturalist and nature in general! :slightly_smiling_face:

I definitely wish you a speedy recovery. I for one am pleased that upon your return home, you thought, “Well, I better check the iNaturalist forum!” We definitely have that in common! :sweat_smile:

6 Likes
97

Quantity - which location is observose (I am in observose Cape Town) - is something iNat has blogged about. Where on the world map are the gaps in iNat coverage.

Quality is much harder to evaluate. A mountain of CNC obs, still unidentified, years later?

98

I imagine a lot of the Yukon observations are by non-residents i,e, tourists. Yukon has a lot of visitors and with its small population the tourist/resident ratio gets really out of wack compared to say Ontario. This would skew the results higher.

99

I don’t think this is what it appears to be. It says “Verifiable iNat observations for 2024 (divided by) US Census Bureau Population Estimates for 2024” This means that all the photos taken in the state (no matter who takes them) are divided by the state population.

So just because my state is dark green (0.11 to 0.32) does NOT mean that the people in Oregon are taking a lot of pictures and posting them. It could just mean that we have so much natural beauty in my state that people from other states visit to take photos and post them to iNat from here.

In the same way, Nevada (which is less than 0.04) has a lot of desert so lots of people visit Nevada, but maybe not for seeing wildlife. The residents in the state may be posting wildlife photos to iNat but because the population is small (compared to its size) the postings divided by population is not large. The photos that I recently took in the Las Vegas area (once I post them to iNat) will count toward Nevada’s observations, not Oregon’s.

Sorry, I read the beginning of this post, made my comment, and then read all the comments - where I see that others have already commented on these issues. (Always late to the party - sigh.)

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Same thought here, biodiversity and abundance, might be interesting factors here, more species, more motivation to ID and log?

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