1. Amanita sp-M23, Mpio. San Luis Teolocholco, Parq. Nac. La Malinche, Tlaxcala edo., México.
discussion
—R. E. Tulloss
brief editors
RET
name
Amanita sp-M23
author
Tulloss et al.
name status
cryptonomen temporarium
english name
"Turpentine Lepidella"
GenBank nos.
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accession
locus
voucher
source
intro
Olive text indicates a specimen that has not been
thoroughly examined (for example, for microscopic details) and marks other places in the text
where data is missing or uncertain.
The following material is based on original research of R. E. Tulloss.
[40/2/2] (10.4-) 10.8 - 15.0 (-16.6) × (5.0-) 6.1 - 7.9 (-10.4) µm, (L = 11.7 - 13.1 µm; L’ = 12.4 µm; W = 6.9 - 7.1 µm; W’ = 7.0 µm; Q = (1.49-) 1.51 - 2.32 (-3.0); Q = 1.70 - 1.87; Q’ = 1.79), ??, ??, ??-walled, amyloid, ellipsoid to elongate, occasionally cylindric, rarely bacilliform, adaxially flattened, sometimes adaxially depressed, sometimes as irregularly formed “giant” spores (e.g., sinuate or “V”-shaped); apiculus sublateral, cylindric; contents ??; ?? in deposit.
ecology
Solitary. At 3100 m elev. In Abies/Pinus forest.
material examined
MÉXICO: TLAXCALA—Mpio. San Luis Teolocholco, Parq. Nac. La Malinche, SW slope of La Malintzi volcano, 14.viii.1998 A. Kong-Luz & A. Montoya-Esquivel s.n. [Tulloss 8-14-98-B] (RET 292-3; TLXM), s.n. [Tulloss 8-14-98-C] (RET 292-4; TLXM).
citations
—R. E. Tulloss
editors
RET
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can be found here.
name
Amanita sp-M23
name status
cryptonomen temporarium
author
Tulloss et al.
english name
"Turpentine Lepidella"
images
1. Amanita sp-M23, Mpio. San Luis Teolocholco, Parq. Nac. La Malinche, Tlaxcala edo., México.
photo
RET - (1) Municipio de San Luis Teolocholco, Parque Nacional La Malinche, Tlaxcala estado, México.
name
Amanita sp-M23
name
Amanita sp-M23
Spore data for collections provisionally identified as: Amanita sp-M23 Tulloss et al.
Each spore data set is intended to comprise a set of measurements from a single specimen made by a single observer;
and explanations prepared for this site talk about specimen-observer pairs associated with each data set.
Combining more data into a single data set is non-optimal because it obscures observer differences
(which may be valuable for instructional purposes, for example) and may obscure instances in which
a single collection inadvertently contains a mixture of taxa.