Kansas Aquatic
Gap: Web Interface User’s Guide
(updated 8 April
2009)
Purpose: The
main products of the Kansas Aquatic Gap project are a geodatabase and web interface. These tools allow users to view (1) habitat
parameters for stream valley segments, (2) sampling locations and (3) predicted
distributions of fish and mussel species in Kansas. In addition, the web interface includes a link
to a database search engine that allows users to query and download biological
data into a spreadsheet friendly format (.csv files). The overarching goal of this database system is
to allow researchers access to spatial data that can be used to test research
questions, evaluate sampling efficiency and guide conservation planning.
Background: The Kansas Aquatic Gap project combines
large-scale stream habitat data with biological data to construct models that predict the occurrences of fish and
mussel species in Kansas streams.
Although there are a number of large databases with distributional data
for fish and mussels in Kansas (i.e., Kansas Department of Wildlife and
Conservation Stream Team surveys, University of Kansas and Sternberg Museum of
Natural History collection records), these collection sites represent only a
small fraction of the streams habitat in the state. As such, developing species management plans
requires an extrapolation of these collection records to areas that have not
been sampled. For example, critical
habitat for a species is usually subjectively determined by including entire
watersheds from which a species in known to occur. Although this process can works, it becomes
difficult to project potential habitat for a species that is patchily
distributed across the landscape. Kansas Aquatic Gap used a combination of hydrologic tools associated with a
geographic information system (GIS) in combination with robust predictive
modeling approaches that use habitat characteristics of streams and their
watersheds to predict distributions of fishes and mussels. Because this approach used both
species distributional data and physical attributes of streams and watersheds,
predicted distributions of species were based on objective criteria. The end product includes
a geodatabase and interactive maps that can be used to evaluate the
conservation status of any stream or river reach in the state. In addition, the construction of relational
databases will provide a resource to begin evaluating factors that are
associated with the disappearance of imperiled species.
Methods: The
majority of the species (fish and mussel) occurrence records in the KS Aquatic
Gap database are from the Kansas Department of Wildlife and Parks Stream Team
surveys. In addition, as part of the
Kansas Aquatic Gap Program we have compiled data from natural history museums,
most of which were from the KU and Sternberg collections. We also have records from six other major
museums that have records from Kansas waters and have compiled mussel data from
the Kansas Department of Health and Environment. From these data, we have mapped the distribution
of collection records in Kansas by 8, 11, and 14-digit HUCs. GIS-derived characteristics of the stream
network were calculated from a modified version of the National Hydrography
Dataset (NHD; USGS 1997). In this data
layer, stream segments were characterized by a variety of biologically relevant
attributes including stream slope, stream order, link magnitude and
sinuosity. This stream network data
layer was used to construct a watershed data layer for every stream segment in
the state of Kansas (> 50,000 watersheds).
Land use for these watersheds based on the NLCD (USGS 1992) and geology
of each watersheds from the STATSGO database (NRCS 1994).
Models of species
distributions were conducted with classification trees (CT) and the maximum entropy
method (Maxent). The output from these
models is a prediction of the occurrence or likelihood of occurrence of each
species for each stream segment in the state.
Stream segments are line features in a GIS that represent stream
sections between tributary confluences. Classification
trees are a simple but robust way to classify ecological groups bases on a suit
of explanatory variables (De’ath and Fabricius 2000). We only used KDWP stream survey data (approximately
1,200 community collections) in the construction of CT because sampling effort
was standardized and species absences resulted from an inability to capture that
species after and extended effort. Although
detection probability was not perfect, we felt confident in grouping sites
based on the presence or absence of a species assuming that if a species was
not detected it was likely rare at best.
Predicted distributions of fishes based on CT explain variation of a
single response variable (i.e., species presence or absence) by repeatedly splitting
the data into more homogeneous gropus, using combinations of explanatory
variables (i.e., stream habitat) that may be categorical and/or numeric. Each group is characterized by a typical
value of the response variable, the number of observations in the group, and
the values of the explanatory variables that define it.
In addition to CT modeling, we used
Maxent to construct models based on species occurrences only. Maxent is a general-purpose machine
learning method with a simple and precise mathematical formulation, and it has
a number of aspects that make it well-suited for species distribution modeling
(Phillips et al. 2006). Because Maxent
only relies on presence only data, we used our entire database on fish species
distributions (approximately 3,400 community collections); albeit we only used
the KDWP stream survey data to model mussel species distributions with
Maxent. The relative importance of
stream habitat variables in these models is assessed by evaluating the
performance of the model when the variable of interest is left out. Caution is suggested in interpreting these
variable weightings because this method does not account for potential
interactions among variables.
Summary of Results: Relevant habitat attributes were defined
for 55,295 stream segments in the state of Kansas. Because the original National Hydrography Dataset (NHD; USGS 1997) overestimated
the occurrence of streams with viable fish and mussel habitat, we excluded 1st
order streams from our analyses and capture of watershed land cover and
geologic data. Habitats from 2nd
order and greater streams include
catchment geology, catchment land cover and stream segment or network
attributes (Appendix I).
Predicted
distributions were generated for 119 fish and 43 mussel species. Classification tree analysis was only used
for fish species that occurred at > 4 sites (N = 99 species). Of the 99 species modeled, 58 generated
significant models (Appendix II). As an
example of insignificant models, many common species resulted in models that predicted
them to occur everywhere (e.g., Lepomis
cyanellus) and rare species were predicted to always be absent (e.g., Ambloplites rupestris); these models
were not significantly different from random.
Classification tree results for each species with a significant model
are provided in Appendix III.
MaxEnt was used to
predict the probability of occurrences for both fish and mussel species. Mean variable importance across fish species
was greatest for geographic coordinates, reflecting regional differences in
fish species distributions (Appendix IV).
For mussels, easting and stream link (an indicator of catchment size)
were, on average, weighted the heaviest among predictor variables (Appendix V).
De’ath, G. and K.E. Fabricius. 2000.
Classification and regression trees: a powerful yet simple technique for
ecological data analysis. Ecology 81:3178-3192.
Gido, K.B., J.A. Falke, R.M. Oakes, and K.J. Hase. 2006.
Fish-habitat relationships across spatial scales in prairie
streams. Hughes, B., P. Seelbach, and L.
Wang (eds.) Influences of Landscapes on Stream Habitats and Biological
Communities, American Fisheries Society Symposium 48:265–285.
NRCS (Natural Resources Conservation Service). 1994. State
soil geographic (STATSGO) database for Kansas. NRCS, Fort Worth, Texas.
Oakes, R.M., K. B. Gido, J.A. Falke, J.D. Olden, and B.L. Brock. 2005. Predictive modeling of stream fish assemblages in the Great Plains. Ecology of Freshwater Fishes 14:361-374.
Phillips, S.J., R.P.
Anderson, R.E. Schapire. 2006. Maximum entropy modeling of species
geographic distributions. Ecological
Modelling 190: 231–259.
USGS (U.S. Geological Survey). 1997. National hydrography
dataset. USGS, Reston, Virginia.
USGS (U.S. Geological Survey). 1998. National Land Cover Data (NLCD). USGS, Reston, Virginia.
Instructions
for the web interface
1) Connect to site in web browser: http://www.konza.ksu.edu/fishecology/
2) Users can navigate by checking available layers and using GIS tools. In addition, there are links to other web resources and this user’s guide
a) There are six main categories of GIS layers that can be viewed. Some of these only appear when zoomed in (County boundaries, TSR, and HUCs).
b) For overlapping layers (e.g., stream habitat and predicted distributions) you can only view one item at a time. All other items must be unchecked. For stream segment habitat characteristics, species distributions, and results from ecological niche modeling, you need to check the main box and box within that menu that you would like to view.
c) Layer categories
i) Boundaries –
contains county boundaries, hydrologic units and township, section and range
ii) Field Sites – point data of sample locations of KDWP and habitat variables taken at those sites
iii) Stream segments – Habitat information quantified for each stream segment. Variable descriptions are given in Appendix I.
iv) CT predictions and occurrences – This layer includes predicted occurrence, based on classification tree (CT) analysis, of fish species for each stream segment (indicated by dark green) and predicted absence (indicated by grey). Observed presences of species are indicated by red. Checking the first box in the drop down list “CT Predictions and Occurrences” is used in concert with the information tool to show a species list with predicted occurrences and absences for a stream segment.
v) MaxEnt Predictions – This layer includes predicted occurrence, based on Maxent analysis, of fish species for each stream segment. Dark green indicates a probability of occurrence > 50%, light green 10% - 50% and grey < 10%.
4) Using GIS tools
a) Zoom in and out, drag screen, full extent view, magnifier and measure tools help you maximize viewing of data layers and are self-explainatory.
b) Identify tool will allow you to capture data from individual stream segments, but the information you acquire will depend on the layers that are selected.
a. Stream segment habitat attributes – left click on identify tool on tool bar. Move hand to stream segment that you want to view data and left click. For example, in the top figure below, a valley segment on Wildcat Creek has been selected. In the bottom figure, the percent grassland for that segment can be viewed by clicking on the expand button (note that the stream segment attribute Grassland (%) is highlighted). In some cases, you might have to drag the mouse over the results to expand the table.
b. Species predictions – predicted occurrences for the fish community can be viewed for each stream segment with the identification tool. However, you must have the Community predictions box check, rather than individual species to view the entire community (See below)
5) Web links (Advanced Query)
a) A query of collection records can be made through the Advanced Query link at the top of the Aquatic Gap web interface. This search engine allows users to query the KDWP database by species names, stream name, county, HUC units and can constrain the search by stream size and year of collection. Leave field blank if you want an unconstrained search. This search engine also provides recognition of species names so users do not need to know exact spelling. For example, I have selected a query of collections from Pottawatomie county
b) After selecting a search constraints, click “send” and a page that asks you to further constrain your search. Check any boxes you want or select all records (e.g., species or stream sizes).
c) View individual records or download all to .csv file.
Appendix I: Habitat attributes quantified for stream segments and used in ecological niche models for Kansas fish and mussel species.
|
Category |
Abbreviation |
Descritpion |
|
Catchment Geology |
C_AWC |
Available water capacity |
|
C_BD |
Bulk density of soils |
|
|
C_KFACT |
Soil erodibility factor |
|
|
C_OM |
Organic matter content of
soils |
|
|
C_PERM |
Soil permeability |
|
|
C_ROCKDEP |
Depth to bedrock |
|
|
C_SLOPE |
Field slope |
|
|
C_TFACT |
Soil loss tolerance
factor |
|
|
C_WEG |
Wind erodibility group |
|
|
C_WTDEP |
Water table depth |
|
|
Stream segment |
DLINK |
Link magnitude of
downstream segment |
|
DOWNORDER |
Strahler order of downstream segment |
|
|
GRADRCHMKM |
Gradient (% change over
length of segment) |
|
|
LINK |
Link magnitude (no. of
stream segments upstream of a given stream segment) |
|
|
STRAHLER |
Strahler order of stream
segment |
|
|
MAX_ELEV |
Maximum elevation |
|
|
SEGMDPTX |
UTM easting coordinate |
|
|
SEGMDPTY |
UTM northing coordinate |
|
|
SHD_AREA_1 |
Drainage area above
segment |
|
|
Catchment land cover |
PCT_CULTIV |
Agricultural land |
|
PCT_FOREST |
Forested land |
|
|
PCT_GRASS |
Rangeland |
|
|
PCT_SHRUB |
Shrubland |
|
|
PCT_URBAN |
Urban land |
|
|
PCT_WETLAN |
Wetlands |
|
|
|
P_OPENH2O |
Water |
Appendix II. Summary results from classification tree analysis to predict fish species occurrence in Kansas streams.
|
Species |
Occurrences |
Classification success |
Correct present |
Correct absent |
Cohen's Κ |
P |
|
Ambloplites rupestris |
6 |
0.995 |
predicted to always be absent |
|||
|
Ameiurus melas |
392 |
0.800 |
0.666 |
0.871 |
0.549 |
<0.001 |
|
Ameiurus natalis |
450 |
0.719 |
0.578 |
0.812 |
0.399 |
<0.001 |
|
Aplodinotus grunniens |
177 |
0.862 |
0.119 |
0.999 |
0.183 |
<0.001 |
|
Campostoma anomalum |
676 |
0.827 |
0.905 |
0.711 |
0.632 |
<0.001 |
|
Carassius auratus |
30 |
0.974 |
predicted to always be absent |
|||
|
Carpiodes carpio |
268 |
0.847 |
0.440 |
0.972 |
0.493 |
<0.001 |
|
Carpiodes cyprinus |
54 |
0.953 |
predicted to always be absent |
|||
|
Catostomus commersonii |
176 |
0.953 |
0.818 |
0.978 |
0.817 |
<0.001 |
|
Cottus carolinae |
4 |
0.996 |
predicted to always be absent |
|||
|
Ctenopharyngodon idella |
12 |
0.989 |
predicted to always be absent |
|||
|
Cycleptus elongatus |
6 |
0.997 |
0.667 |
0.999 |
0.726 |
<0.001 |
|
Cyprinella camura |
60 |
0.979 |
0.667 |
0.996 |
0.758 |
<0.001 |
|
Cyprinus carpio |
560 |
0.720 |
0.650 |
0.789 |
0.439 |
<0.001 |
|
Cyprinella lutrensis |
845 |
0.874 |
0.943 |
0.675 |
0.652 |
<0.001 |
|
Cyprinella spiloptera |
6 |
0.997 |
0.833 |
0.998 |
0.768 |
<0.001 |
|
Dorosoma cepedianum |
282 |
0.828 |
0.422 |
0.963 |
0.455 |
<0.001 |
|
Erimystax x-punctatus |
10 |
0.997 |
0.800 |
0.999 |
0.841 |
<0.001 |
|
Etheostoma blennioides |
13 |
0.993 |
0.538 |
0.998 |
0.633 |
<0.001 |
|
Etheostoma chlorosoma |
9 |
0.992 |
predicted to always be absent |
|||
|
Etheostoma cragini |
141 |
0.942 |
0.759 |
0.968 |
0.731 |
<0.001 |
|
Etheostoma flabellare |
41 |
0.964 |
predicted to always be absent |
|||
|
Etheostoma gracile |
4 |
0.996 |
predicted to always be absent |
|||
|
Etheostoma nigrum |
79 |
0.953 |
0.354 |
0.997 |
0.489 |
<0.001 |
|
Etheostoma spectabile |
524 |
0.883 |
0.906 |
0.863 |
0.766 |
<0.001 |
|
Etheostoma stigmaeum |
3 |
0.997 |
predicted to always be absent |
|||
|
Etheostoma whipplei |
18 |
0.984 |
predicted to always be absent |
|||
|
Etheostoma zonale |
8 |
0.997 |
0.875 |
0.998 |
0.822 |
<0.001 |
|
Etheostoma zonale |
239 |
0.936 |
0.837 |
0.962 |
0.805 |
<0.001 |
|
Fundulus notatus |
150 |
0.934 |
0.747 |
0.963 |
0.711 |
<0.001 |
|
Gambusia affinis |
400 |
0.833 |
0.740 |
0.883 |
0.630 |
<0.001 |
|
Hybognathus hankinsoni |
10 |
0.991 |
predicted to always be absent |
|||
|
Hybognathus placitus |
45 |
0.985 |
0.689 |
0.997 |
0.777 |
<0.001 |
|
Hypentelium nigricans |
7 |
0.994 |
predicted to always be absent |
|||
|
Ictiobus bubalus |
120 |
0.913 |
0.275 |
0.988 |
0.363 |
<0.001 |
|
Ictiobus cyprinellus |
41 |
0.964 |
predicted to always be absent |
|||
|
Ictiobus niger |
59 |
0.948 |
predicted to always be absent |
|||
|
Ictalurus punctatus |
577 |
0.845 |
0.868 |
0.821 |
0.690 |
<0.001 |
|
Labiesthes sicculus |
153 |
0.895 |
0.588 |
0.943 |
0.542 |
<0.001 |
|
Lepomis cyanellus |
915 |
0.805 |
predicted to occur at all locations |
|||
|
Lepomis gulosus |
57 |
0.950 |
predicted to always be absent |
|||
|
Lepomis humilis |
475 |
0.736 |
0.632 |
0.811 |
0.449 |
<0.001 |
|
Lepomis macrochirus |
527 |
0.741 |
0.698 |
0.779 |
0.478 |
<0.001 |
|
Lepomis megalotis |
368 |
0.857 |
0.766 |
0.900 |
0.670 |
<0.001 |
|
Lepomis microlophus |
16 |
0.986 |
predicted to always be absent |
|||
|
Lepisosteus oculatus |
5 |
0.996 |
predicted to always be absent |
|||
|
Lepisosteus osseus |
160 |
0.891 |
0.375 |
0.975 |
0.437 |
<0.001 |
|
Lepisosteus platostomus |
30 |
0.974 |
predicted to always be absent |
|||
|
Luxilus cardinalis |
36 |
0.988 |
0.750 |
0.995 |
0.788 |
<0.001 |
|
Luxilus cornutus |
103 |
0.965 |
0.738 |
0.987 |
0.773 |
<0.001 |
|
Lythrurus umbratilis |
264 |
0.891 |
0.765 |
0.929 |
0.694 |
<0.001 |
|
Lythrurus umbratilis |
12 |
0.998 |
0.833 |
1.000 |
0.908 |
<0.001 |
|
Macrohybopsis storeiana |
16 |
0.986 |
predicted to always be absent |
|||
|
Macrohybopsis tetranema |
16 |
0.986 |
predicted to always be absent |
|||
|
Menidia beryllina |
8 |
0.993 |
predicted to always be absent |
|||
|
Micropterus dolomieu |
11 |
0.990 |
predicted to always be absent |
|||
|
Micropterus punctulatus |
73 |
0.964 |
0.575 |
0.991 |
0.653 |
<0.001 |
|
Micropterus salmoides |
604 |
0.697 |
0.808 |
0.570 |
0.383 |
<0.001 |
|
Minytrema melanops |
39 |
0.966 |
predicted to always be absent |
|||
|
Morone americana |
26 |
0.977 |
predicted to always be absent |
|||
|
Morone chryspos |
72 |
0.946 |
0.222 |
0.995 |
0.325 |
<0.001 |
|
Morone hybrid |
9 |
0.992 |
predicted to always be absent |
|||
|
Moxostoma carinatum |
4 |
0.996 |
predicted to always be absent |
|||
|
Moxostoma erythrurum |
154 |
0.888 |
0.584 |
0.936 |
0.522 |
<0.001 |
|
Moxostoma macrolepidotum |
114 |
0.927 |
0.360 |
0.990 |
0.464 |
<0.001 |
|
Nocomis asper |
5 |
0.996 |
predicted to always be absent |
|||
|
Nocomis biguttatus |
9 |
0.992 |
predicted to always be absent |
|||
|
Notropis athernoides |
97 |
0.970 |
0.722 |
0.993 |
0.789 |
<0.001 |
|
Notropis bairdi |
4 |
0.996 |
predicted to always be absent |
|||
|
Notropis boops |
21 |
0.996 |
0.810 |
0.999 |
0.870 |
<0.001 |
|
Notropis buchanani |
22 |
0.981 |
predicted to always be absent |
|||
|
Notemigonus crysoleucas |
117 |
0.897 |
predicted to always be absent |
|||
|
Notropis dorsalis |
18 |
0.984 |
predicted to always be absent |
|||
|
Noturus exilis |
117 |
0.966 |
0.786 |
0.986 |
0.806 |
<0.001 |
|
Noturus flavus |
142 |
0.929 |
0.613 |
0.974 |
0.643 |
<0.001 |
|
Noturus nocturnus |
43 |
0.977 |
0.395 |
1.000 |
0.557 |
<0.001 |
|
Notropis nubilus |
5 |
0.998 |
1.000 |
0.998 |
0.832 |
<0.001 |
|
Notropis percobromus |
77 |
0.966 |
0.584 |
0.993 |
0.680 |
<0.001 |
|
Noturus placidus |
5 |
0.996 |
predicted to always be absent |
|||
|
Notropis stramineus |
538 |
0.850 |
0.812 |
0.883 |
0.697 |
<0.001 |
|
Notropis topeka |
32 |
0.972 |
predicted to always be absent |
|||
|
Notropis volucellus |
35 |
0.981 |
0.543 |
0.995 |
0.624 |
<0.001 |
|
Percina caprodes |
232 |
0.876 |
0.720 |
0.916 |
0.625 |
<0.001 |
|
Percina copelandi |
40 |
0.965 |
predicted to always be absent |
|||
|
Percina phoxocephala |
156 |
0.940 |
0.814 |
0.960 |
0.754 |
<0.001 |
|
Phenacobius mirabilis |
507 |
0.773 |
0.759 |
0.784 |
0.542 |
<0.001 |
|
Phoxinus erythrogaster |
36 |
0.986 |
0.667 |
0.996 |
0.743 |
<0.001 |
|
Pimephales notatus |
503 |
0.849 |
0.859 |
0.841 |
0.695 |
<0.001 |
|
Pimephales promelas |
521 |
0.835 |
0.814 |
0.852 |
0.667 |
<0.001 |
|
Pimephales tenellus |
49 |
0.974 |
0.633 |
0.990 |
0.668 |
<0.001 |
|
Pimephales vigilax |
223 |
0.908 |
0.641 |
0.973 |
0.677 |
<0.001 |
|
Pomoxis annularis |
242 |
0.787 |
predicted to always be absent |
|||
|
Pomoxis nigromaculatus |
43 |
0.962 |
predicted to always be absent |
|||
|
Pylodictis olivaris |
292 |
0.860 |
0.801 |
0.880 |
0.650 |
<0.001 |
|
Sander canadensis |
3 |
0.997 |
predicted to always be absent |
|||
|
Sander canadensis |
10 |
0.991 |
predicted to always be absent |
|||
|
Sander vitreus |
20 |
0.982 |
predicted to always be absent |
|||
|
Scaphirhynchus
platorynchus |
4 |
0.999 |
1.000 |
0.999 |
0.888 |
<0.001 |
|
Semotilus atromaculatus |
356 |
0.896 |
0.871 |
0.908 |
0.763 |
<0.001 |
Appendix III: Results of classification trees used to predict the occurrence of fish species in Kansas streams. Predictor variables at each node represent habitat variables described in Appendix I.
Ameiurus melas
Ameiurus natalis
Aplodinotus grunniens
Campostoma anomalum
Carpiodes carpio
Catostomus commersonii
Cycleptus elongatus
Cyprinella camura
Cyprinella lutrensis
Cyprinella spiloptera
Cyprinus carpio
Dorosoma cepedianum
Erimystax x-punctatus
Etheostoma blennioides
Etheostoma cragini
Etheostoma nigrum
Etheostoma spectabile
Etheostoma zonale
Fundulus kansae
Fundulus notatus
Gambusia affinis
Hybognathus placitus
Ictiobus bubalus
Ictalurus punctatus
Labiesthes sicculus
Lepomis humilis
Lepomis macrochirus
Lepomis megalotis
Lepisosteus osseus
Luxilus cardinalis
Luxilus cornutus
Lythrurus umbratilis
Macrhybopsis aestivalis
Micropterus punctulatus
Micropterus salmoides
Morone chrysops
Moxostoma erythrurum
Notropis athernoides
Notropis boops
Notropis nubilus
Notropis percobromus
Notropis stramineus
Notropis volucellus
Noturus exilis
Noturus flavus
Noturus nocturnus
Percina caprodes
Percina phoxocephala
Phenacobius mirabilis
Phoxinus erythrogaster
Pimephales notatus
Pimephales promelas
Pimephales tenellus
Pimephales vigilax
Pylodictis olivaris
Scaphirhynchus platorynchus
Semotilus atromaculatus
Appendix IV: Results from maximum entropy (Maxent) niche modeling of fish species. Variable importance, derived by evaluating the change in model performance when variable is left out. Higher value indicates greater importance.
|
Species |
C_AWC |
C_BD |
C_KFACT |
C_OM |
C_PERM |
C_ROCKDEP |
C_SLOPE |
C_TFACT |
C_WEG |
C_WTDEP |
|
Ambloplites rupestris |
0.0 |
0.2 |
0.0 |
2.7 |
0.0 |
4.5 |
0.0 |
0.0 |
0.2 |
0.0 |
|
Ameiurus melas |
1.3 |
0.7 |
0.2 |
5.4 |
1.3 |
0.4 |
4.9 |
6.4 |
1.3 |
0.7 |
|
Ameiurus natalis |
2.0 |
2.9 |
0.2 |
0.7 |
2.2 |
2.4 |
3.6 |
2.3 |
2.7 |
1.0 |
|
Aplodinotus grunniens |
0.0 |
1.2 |
8.3 |
2.0 |
3.6 |
4.5 |
0.8 |
1.8 |
1.4 |
2.5 |
|
Campostoma anomalum |
1.0 |
1.4 |
0.0 |
0.0 |
6.7 |
10.2 |
2.7 |
3.5 |
0.8 |
4.0 |
|
Carassius auratus |
0.1 |
0.0 |
0.1 |
1.2 |
0.3 |
0.6 |
10.6 |
6.5 |
0.0 |
0.1 |
|
Carpiodes carpio |
0.0 |
1.7 |
0.0 |
2.8 |
2.7 |
5.2 |
2.1 |
16.2 |
2.0 |
2.6 |
|
Carpiodes cyprinus |
0.0 |
0.2 |
2.4 |
0.0 |
0.3 |
14.7 |
2.8 |
30.0 |
0.2 |
0.2 |
|
Carpiodes velifer |
0.0 |
0.1 |
0.0 |
0.0 |
0.0 |
3.8 |
0.2 |
2.7 |
0.0 |
0.0 |
|
Catostomus commersonii |
0.3 |
2.9 |
0.8 |
0.5 |
1.0 |
1.0 |
2.3 |
0.4 |
1.0 |
3.2 |
|
Cottus carolinae |
0.0 |
0.0 |
0.0 |
17.1 |
0.0 |
11.9 |
0.0 |
0.0 |
0.0 |
0.0 |
|
Ctenopharyngodon idella |
0.0 |
0.0 |
0.0 |
4.3 |
0.9 |
29.5 |
0.0 |
27.9 |
0.0 |
4.3 |
|
Cycleptus elongatus |
0.0 |
0.0 |
0.0 |
9.2 |
0.5 |
6.5 |
0.5 |
1.5 |
2.2 |
0.0 |
|
Cyprinella camura |
0.0 |
0.2 |
1.0 |
0.4 |
0.1 |
0.0 |
1.3 |
0.6 |
0.6 |
8.7 |
|
Cyprinus carpio |
0.0 |
0.6 |
0.0 |
1.3 |
0.7 |
14.5 |
0.0 |
16.2 |
0.0 |
0.7 |
|
Cyprinella lutrensis |
0.0 |
3.1 |
3.9 |
0.0 |
0.0 |
0.0 |
22.7 |
0.0 |
0.0 |
7.2 |
|
Cyprinella spiloptera |
1.2 |
0.3 |
0.0 |
0.5 |
0.6 |
6.4 |
0.5 |
0.0 |
0.1 |
0.0 |
|
Dorosoma cepedianum |
0.9 |
0.0 |
3.5 |
12.8 |
2.9 |
8.8 |
6.9 |
4.1 |
1.5 |
1.1 |
|
Dorosoma petenense |
0.0 |
4.0 |
0.0 |
1.2 |
0.0 |
9.1 |
0.0 |
1.0 |
5.6 |
0.6 |
|
Erimystax x-punctatus |
0.0 |
0.0 |
1.4 |
0.0 |
0.0 |
0.5 |
0.1 |
0.0 |
0.6 |
27.8 |
|
Esox lucius |
0.4 |
0.0 |
0.0 |
2.9 |
0.0 |
0.0 |
0.0 |
0.4 |
0.0 |
0.0 |
|
Etheostoma blennioides |
0.0 |
0.0 |
1.2 |
3.2 |
0.0 |
0.1 |
0.1 |
0.0 |
0.3 |
1.4 |
|
Etheostoma chlorosoma |
0.0 |
1.4 |
0.1 |
4.7 |
2.0 |
12.0 |
2.2 |
0.7 |
0.0 |
26.0 |
|
Etheostoma cragini |
0.0 |
0.0 |
0.2 |
0.1 |
0.5 |
2.5 |
0.0 |
0.2 |
0.0 |
0.5 |
|
Etheostoma flabellare |
0.9 |
0.0 |
0.9 |
1.2 |
0.3 |
3.9 |
2.9 |
25.5 |
2.1 |
1.7 |
|
Etheostoma gracile |
0.0 |
2.0 |
0.0 |
2.8 |
0.6 |
0.2 |
0.0 |
0.0 |
0.0 |
0.0 |
|
Etheostoma nigrum |
0.1 |
2.0 |
3.2 |
1.2 |
0.3 |
4.8 |
2.3 |
3.3 |
3.0 |
11.4 |
|
Etheostoma punctulatum |
0.0 |
0.0 |
0.0 |
7.7 |
0.0 |
8.4 |
0.0 |
0.0 |
0.2 |
0.0 |
|
Etheostoma spectabile |
0.0 |
4.0 |
1.0 |
0.1 |
2.2 |
12.9 |
0.3 |
3.0 |
0.0 |
8.5 |
|
Etheostoma stigmaeum |
0.0 |
2.6 |
0.0 |
8.1 |
0.9 |
21.7 |
1.1 |
0.0 |
15.6 |
0.0 |
|
Etheostoma whipplei |
0.3 |
0.0 |
0.6 |
0.0 |
0.5 |
0.5 |
0.2 |
3.8 |
0.0 |
10.4 |
|
Etheostoma zonale |
0.0 |
0.2 |
1.3 |
0.8 |
0.0 |
0.1 |
0.5 |
0.0 |
0.0 |
4.9 |
|
Etheostoma zonale |
0.7 |
0.3 |
0.3 |
1.9 |
1.1 |
0.7 |
3.5 |
1.5 |
6.6 |
0.4 |
|
Fundulus notatus |
0.1 |
0.0 |
0.3 |
0.1 |
0.3 |
0.4 |
0.2 |
1.9 |
0.2 |
1.5 |
|
Gambusia affinis |
0.6 |
0.2 |
0.0 |
0.0 |
0.4 |
2.0 |
16.8 |
0.9 |
1.5 |
0.1 |
|
Hiodon alosoides |
0.0 |
0.1 |
0.0 |
0.5 |
|