In [1]:
from tavernaPlayerClient import *
In [2]:
c = Client('http://dev.at.biovel.eu', 'player', 'player')
In [3]:
workflows = c.workflows
In [4]:
for w in workflows:
    print str(w.identifier) + ' = ' + w.title
1 = Various output types
2 = Ecological niche modelling workflow v20
4 = Select Model For Me with components
5 = Define with PartitionFinder, infer and validate Phylogeny - short run
6 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime
10 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime
11 = A workflow
12 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime
13 = Bioclim workflow with interaction
15 = A workflow
16 = A workflow
19 = BioVeL ESW DIFF - ENM Statistical Workflow with raster difference computation
20 = BioVeL ESW STACK - ENM Statistical Workflow with raster stack computation
21 = Data Refinement Workflow v13
24 = Biome-BGC ESI Regulation, test & demo version 1.0
25 = Define with PartitionFinder, infer and validate Phylogeny - short run
31 = Biome-BGC CARBON test & demo version 1.0 annotated
32 = Select Model For Me with components
33 = Bioclim workflow with interaction
36 = Phylogenetic Inference:Select Model For me- no component
40 = Phylogenetic Inference:Select Model For me- no component no questions
42 = Ecological niche modelling workflow v21
43 = Ecological niche modelling workflow
46 = [BETA] Data Refinement Workflow v14
48 = Matrix Population Model analysis v10
49 = WebDAV Component Test
50 = A workflow
52 = Multi-interaction
53 = Partitioning environmental sequencing data using categorical and phylogenetic information using PhyloH with parsing Qiime
54 = All File Lister using external tool
56 = All File Lister using external tool
57 = Ecological niche modelling workflow
58 = Data Refinement Workflow v13
59 = Biome-BGC SA test & demo version 1.0 annotated
97 = A workflow
98 = Biome-BGC MCE test & demo version 1.1 annotated
103 = Data Refinement Workflow v14
105 = A workflow
107 = A workflow
115 = Data Refinement Workflow v14
117 = A workflow
118 = Simple ask
119 = BioVeL ESW STACK - ENM Statistical Workflow with raster stack computation
120 = Retrieving FASTA format
130 = Data Refinement Workflow v15
131 = List output
136 = Biome-BGC ESI ALL version 1.2.1
In [5]:
w = c.get_workflow(48)
In [8]:
print w.title
Matrix Population Model analysis v10
In [9]:
print w.description
The Matrix Population Models Workflow provides an environment to perform several analyses on a stage-matrix with no density dependence:
- Eigen analysis;
- Age specific survival;
- Generation time (T);
- Net reproductive rate (Ro);
- Transient Dynamics;
- Bootstrap of observed census transitions (Confidence intervals of lambda);
- Survival curve;
- Keyfitz delta;
- Cohen's cumulative distance.

This workflow requires an instance of Rserve on localhost

This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359. 

This workflow uses R packages ‘popbio’ (Stubben & Milligan 2007; Stubben, Milligan & Nantel 2011) and 'popdemo' (Stott, Hodgson and Townley 2013).

References:

Caswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233.

Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts.

de Kroon, H. J., A. Plaiser, J. van Groenendael, and H. Caswell. 1986. Elasticity: The relative contribution of demographic parameters to population growth rate. Ecology 67: 1427-1431. 

Horvitz, C., D.W. Schemske, and Hal Caswell. 1997. The relative "importance" of life-history stages to population growth: Prospective and retrospective analyses. In S. Tuljapurkar and H. Caswell. Structured population models in  terrestrial and freshwater systems. Chapman and Hall, New York.

Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopaedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423

Mesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468.

Oostermeijer J.G.B., M.L. Brugman; E.R. de Boer; H.C.M. Den Nijs. 1996. Temporal and Spatial Variation in the Demography of Gentiana pneumonanthe, a Rare Perennial Herb. The Journal of Ecology, Vol. 84(2): 153-166.

Stott, I., S. Townley and D.J. Hodgson 2011. A framework for studying transient dynamics of population projection matrix models. Ecology Letters 14: 959–970

Stubben, C & B. Milligan. 2007. Estimating and Analysing Demographic Models Using the popbio Package in R. Journal of Statistical Software 22 (11): 1-23

Stubben, C., B. Milligan, P. Nantel. 2011. Package ‘popbio’. Construction and analysis of matrix population models. Version 2.3.1

van Groenendael, J., H. de Kroon, S. Kalisz, and S. Tuljapurkar. 1994. Loop analysis: Evaluating life history pathways in population projection matrices. Ecology 75: 2410-2415.
In [10]:
rt = w.run_template
In [11]:
print rt.inputs
{u'longTermYears': u'50', u'label': u'Gentiana pneumonanthe, Terschelling', u'iterations': u'10000', u'stageMatrixFile': u'0.0000\t0.0000\t0.0000\t7.6660\t0.0000\r\n0.0579\t0.0100\t0.0000\t8.5238\t0.0000\r\n0.4637\t0.8300\t0.9009\t0.2857\t0.8604\r\n0.0000\t0.0400\t0.0090\t0.6190\t0.1162\r\n0.0000\t0.0300\t0.0180\t0.0000\t0.0232', u'stages': u'[S, J, V, G, D]', u'shortTermYears': u'10'}
In [12]:
r = w.run('Example notebook run', {})
In [13]:
import imghdr
from IPython.display import *
In [17]:
for k in sorted(r.outputs):
    v = r.outputs[k]
    display_html(HTML('<h2>' + k + '</h2>'))
    guess = imghdr.what('', v)
    if guess == 'png':
        display_png(Image(v))
    else:
        print v

cohen_cumulative_distance

2.7622718909505335

confidence_interval_95pc_of_lambda

     2.5%     97.5% 
0.9577327 1.4814221 

damping_ratio

2.0920247472869264

eigenanalysis

$lambda1
[1] 1.237596

$stable.stage
         S          J          V          G          D 
0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 

$sensitivities
           S          J          V           G          D
S 0.00000000 0.00000000  0.0000000 0.005956842 0.00000000
J 0.14133539 0.16509663  0.0000000 0.022817127 0.00000000
V 0.08083208 0.09442153  0.3753343 0.013049498 0.00789583
G 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854
D 0.00000000 0.33985571  1.3509578 0.000000000 0.02841982

$elasticities
            S           J          V           G            D
S 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000
J 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000
V 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301
G 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311
D 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586

$repro.value
        S         J         V         G         D 
 1.000000  3.830406  2.190674 66.184553  7.884991 

$damping.ratio
[1] 2.092025


eigenanalysis_elasticity_matrix

eigenanalysis_sensitivity_matrix_1

eigenanalysis_sensitivity_matrix_2

elasticity_matrix

            S           J          V           G            D
S 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000
J 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000
V 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301
G 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311
D 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586

elasticity_plot

fundamental_matrix

$N
           S          J          V          G          D
S 1.00000000  0.0000000  0.0000000  0.0000000  0.0000000
J 0.05848485  1.0101010  0.0000000  0.0000000  0.0000000
V 6.88603853 11.9918695 13.3528343 10.0128734 12.9527790
G 0.20805087  0.4661775  0.3904662  2.9174703  0.6909984
D 0.12868882  0.2520032  0.2460596  0.1845124  1.2624386

$var
             S            J           V           G           D
S   0.00000000   0.00000000   0.0000000   0.0000000   0.0000000
J   0.05624588   0.01020304   0.0000000   0.0000000   0.0000000
V 129.59269836 164.45409019 164.9453506 157.1299726 165.1853617
G   0.96262845   2.03661916   1.7354172   5.5941629   2.8634574
D   0.17967383   0.32076827   0.3146653   0.2473139   0.3313126

$cv
         S        J         V         G         D
S 0.000000      NaN       NaN       NaN       NaN
J 4.055104 0.100000       NaN       NaN       NaN
V 1.653183 1.069388 0.9618261 1.2519033 0.9922539
G 4.715848 3.061284 3.3737933 0.8107017 2.4488847
D 3.293833 2.247448 2.2797338 2.6952479 0.4559411

$meaneta
        S         J         V         G         D 
 8.281263 13.720151 13.989360 13.114856 14.906216 

$vareta
       S        J        V        G        D 
143.2630 181.0125 181.4811 177.0582 181.0617 


generation_time

8.055186065644556

histogram_ci_95pc_of_lambda

keyfitz_delta

0.36601706946206447

lambda

1.2375958894297714

long_term_logarithmic_stage_vector_plot

long_term_proportional_stage_vector_plot

net_reproductive_rate

5.568521857873462

population_projection

$lambda
[1] 1.237596

$stable.stage
         S          J          V          G          D 
0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 

$stage.vectors
    0        1         2         3         4          5          6          7
S  69 160.9860 178.89071 190.39177 223.94351  268.87303  328.10282  403.26362
J 111 184.1049 210.07013 224.15446 262.26727  314.54803  383.52967  471.21993
V 100 257.2122 471.11791 697.69349 924.15509 1181.00728 1483.42272 1848.82326
G  21  23.3356  24.83587  29.21256  35.07344   42.79974   52.60418   64.88797
D  43   6.1276  10.29513  15.02107  19.63161   24.95826   31.27360   38.93305
           8          9         10         11        12        13        14
S  497.43117  614.64363  760.10376  940.36011 1163.5840 1439.9273 1781.9775
J  581.15324  718.03301  887.92505 1098.47255 1359.2167 1682.0141 2081.5668
V 2295.74724 2845.73491 3524.54805 4363.55062 5401.2496 6685.1188 8273.8036
G   80.17788   99.15259  122.66633  151.78502  187.8329  232.4521  287.6763
D   48.31866   59.87904   74.15341   91.79998  113.6278  140.6352  174.0553
          15         16         17         18         19         20         21
S  2205.3263  2729.2780  3377.7286  4180.2544  5173.4605  6402.6505  7923.8921
J  2576.0871  3188.1244  3945.5917  4883.0374  6043.2207  7479.0614  9256.0535
V 10239.8194 12672.8732 15683.9637 19410.4492 24022.3160 29729.9336 36793.6519
G   356.0237   440.6116   545.2980   674.8579   835.2009  1033.6410  1279.2297
D   215.4136   266.5970   329.9405   408.3337   505.3526   625.4225   774.0204
          22        23        24        25         26         27         28
S  9806.5752 12136.577 15020.177 18588.909  23005.557  28471.583  35236.314
J 11455.2524 14176.973 17545.362 21714.068  26873.241  33258.213  41160.228
V 45535.6772 56354.770 69744.433 86315.425 106823.616 132204.468 163615.706
G  1583.1694  1959.324  2424.851  3000.986   3714.008   4596.441   5688.536
D   957.9246  1185.524  1467.199  1815.800   2247.226   2781.158   3441.950
          29        30         31        32         33        34        35
S  43608.317  53969.47  66792.399  82662.00 102302.150 126608.72 156690.43
J  50939.728  63042.80  78021.508  96559.10 119501.143 147894.12 183033.16
V 202490.126 250600.95 310142.703 383831.33 475028.081 587892.80 727573.71
G   7040.109   8712.81  10782.938  13344.92  16515.617  20439.66  25296.04
D   4259.743   5271.84   6524.408   8074.58   9993.067  12367.38  15305.82
         36         37         38         39         40         41        42
S 193919.43  239993.90  297015.46  367585.11  454921.82  563009.38  696778.1
J 226521.08  280341.56  346949.57  429383.36  531403.08  657662.26  813920.1
V 900442.24 1114383.61 1379156.58 1706838.51 2112376.32 2614268.25 3235407.6
G  31306.27   38744.52   47950.05   59342.79   73442.39   90892.00  112487.6
D  18942.42   23443.06   29013.03   35906.41   44437.62   54995.82   68062.6
          43        44        45        46        47         48         49
S  862329.70 1067215.7 1320781.8 1634594.1 2022966.9  2503615.5  3098464.3
J 1007304.19 1246635.5 1542831.0 1909401.3 2363067.2  2924522.3  3619376.7
V 4004127.20 4955491.4 6132895.7 7590046.6 9393410.4 11625246.1 14387356.8
G  139214.15  172290.9  213226.5  263888.2  326586.9   404182.7   500214.8
D   84233.99  104247.6  129016.5  159670.2  197607.2   244557.9   302663.8

$pop.sizes
 [1] 3.440000e+02 6.317663e+02 8.952097e+02 1.156473e+03 1.465071e+03
 [6] 1.832186e+03 2.278933e+03 2.827128e+03 3.502828e+03 4.337443e+03
[11] 5.369397e+03 6.645968e+03 8.225511e+03 1.018015e+04 1.259908e+04
[16] 1.559267e+04 1.929748e+04 2.388252e+04 2.955693e+04 3.657955e+04
[21] 4.527071e+04 5.602685e+04 6.933860e+04 8.581317e+04 1.062020e+05
[26] 1.314352e+05 1.626636e+05 2.013119e+05 2.491427e+05 3.083380e+05
[31] 3.815979e+05 4.722640e+05 5.844719e+05 7.233401e+05 8.952027e+05
[36] 1.107899e+06 1.371131e+06 1.696907e+06 2.100085e+06 2.599056e+06
[41] 3.216581e+06 3.980828e+06 4.926656e+06 6.097209e+06 7.545881e+06
[46] 9.338751e+06 1.155760e+07 1.430364e+07 1.770212e+07 2.190808e+07

$pop.changes
 [1] 1.836530 1.416995 1.291846 1.266844 1.250579 1.243833 1.240549 1.239006
 [9] 1.238269 1.237917 1.237750 1.237669 1.237631 1.237613 1.237604 1.237600
[17] 1.237598 1.237597 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596
[25] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596
[33] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596
[41] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596
[49] 1.237596


projection_matrix

sensitivity_matrix

           S          J          V           G          D
S 0.00000000 0.00000000  0.0000000 0.005956842 0.00000000
J 0.14133539 0.16509663  0.0000000 0.022817127 0.00000000
V 0.08083208 0.09442153  0.3753343 0.013049498 0.00789583
G 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854
D 0.00000000 0.33985571  1.3509578 0.000000000 0.02841982

sensitivity_plot

short_term_stage_vector_plot

stable_stage_distribution

stage_matrix

       S    J      V      G      D
S 0.0000 0.00 0.0000 7.6660 0.0000
J 0.0579 0.01 0.0000 8.5238 0.0000
V 0.4637 0.83 0.9009 0.2857 0.8604
G 0.0000 0.04 0.0090 0.6190 0.1162
D 0.0000 0.03 0.0180 0.0000 0.0232

survival_curve_plot

In [ ]: