Niche modeling: predictions from statistical distributions
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton Fla. [u.a.]
Chapman & Hall/CRC
2007
|
| Schriftenreihe: | Chapman & Hall/CRC mathematical and computational biology series
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 191 - 197 |
| Beschreibung: | 201 S. Ill., Kt., graph. Darst. |
| ISBN: | 1584884940 9781584884941 |
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| 100 | 1 | |a Stockwell, David Russell Bancroft |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Niche modeling |b predictions from statistical distributions |c David Stockwell |
| 264 | 1 | |a Boca Raton Fla. [u.a.] |b Chapman & Hall/CRC |c 2007 | |
| 300 | |a 201 S. |b Ill., Kt., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Chapman & Hall/CRC mathematical and computational biology series | |
| 500 | |a Literaturverz. S. 191 - 197 | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Niche (Ecology) |x Computer simulation | |
| 650 | 4 | |a Niche (Ecology) |x Mathematical models | |
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Datensatz im Suchindex
| _version_ | 1804137479592214528 |
|---|---|
| adam_text | Contents
0.1
Preface
.............................. xix
0.1.1
Summary of chapters
................... xix
1
Functions
1
1.1
Elements
............................. 1
1.1.1
Factor
........................... 1
1.1.2
Complex
.......................... 2
1.1.3
Raw
............................ 2
1.1.4
Vectors
.......................... 2
1.1.5
Lists
............................ 3
1.1.6
Data frames
........................ 3
1.1.7
Time series
........................ 3
1.1.8
Matrix
........................... 4
1.2
Operations
............................ 4
1.3
Functions
............................. 6
1.4
Ecological models
........................ 9
1.4.1
Preferences
........................ 11
1.4.2
Stochastic functions
................... 11
1.4.3
Random fields
...................... 18
1.5
Summary
............................. 21
2
Data
23
2.1
Creating
............................. 24
2.2
Entering data
.......................... 25
2.3
Queries
.............................. 26
2.4
Joins
............................... 28
2.5
Loading and saving a database
................. 29
2.6
Summary
............................. 29
3
Spatial
31
3.1
Data types
............................ 31
3.2
Operations
............................ 34
3.2.1
Rasterizing
........................ 37
3.2.2
Overlay
.......................... 37
3.2.3
Proximity
......................... 39
3.2.4
Cropping
......................... 40
3.2.5
Palette swapping
..................... 40
3.3
Summary
............................. 44
4
Topology
45
4.1
Formalism
............................ 45
4.2
Topology
............................. 47
4.3
Hutchinsonian niche
....................... 47
4.3.1
Species space
....................... 48
4.3.2
Environmental space
................... 48
4.3.3
Topological generalizations
............... 49
4.3.4
Geographic space
..................... 49
4.3.5
Relationships
....................... 50
4.4
Environmental envelope
..................... 51
4.4.1
Relevant variables
.................... 51
4.4.2
Tails of the distribution
................. 51
4.4.3
Independence
....................... 52
4.5
Probability distribution
..................... 52
4.5.1
Dynamics
......................... 53
4.5.2
Generalized linear models
................ 54
4.6
Machine learning methods
................... 57
4.7
Data mining
........................... 58
4.7.1
Decision trees
....................... 59
4.7.2
Clustering
......................... 59
4.7.3
Comparison
........................ 59
4.8
Post-Hutchinsonian niche
.................... 60
4.8.1
Product space
...................... 61
4.9
Summary
............................. 63
5
Environmental data collections
65
5.1
Datasets
............................. 66
5.1.1
Global ecosystems database
............... 88
5.1.2
Worldclim
......................... 89
5.1.3
World ocean atlas
.................... 90
5.1.4
Continuous fields
..................... 90
5.1.5
Hydrolkm
......................... 91
5.1.6
WhyWhere
........................ 91
5.2
Archives
............................. 91
5.2.1
Traffic
........................... 92
5.2.2
Management
....................... 92
5.2.3
Interaction
........................ 92
5.2.4
Updating
......................... 92
5.2.5
Legacy
........................... 92
5.2.6
Example: WhyWhere archive
.............. 93
5.2.7
Browsing
......................... 93
5.2.8
Format
.......................... 94
5.2.9
Meta data
......................... 94
5.2.10
Operations
........................ 95
5.3
Summary
............................. 95
Examples
97
6.0.1
Model skill
........................ 97
6.0.2
Calculating accuracy
................... 99
6.1
Predicting house prices
..................... 99
6.1.1
Analysis
.......................... 100
6.1.2
Ρ
data and no mask
................... 104
6.1.3
Presence and absence (PA) data
............ 105
6.1.4
Interpretation
....................... 106
6.2
Brown Treesnake
......................... 107
6.2.1
Predictive model
..................... 107
6.3
Invasion of Zebra Mussel
.................... 109
6.4
Observations
........................... 113
Bias
115
7.1
Range shift
............................ 116
7.1.1
Example: climate change
................ 116
7.2
Range-shift Model
........................ 117
7.3
Forms of bias
........................... 120
7.3.1
Width
r
and width error
................. 120
7.3.2
Shift
s
and shift error
.................. 123
7.3.3
Proportional pe
...................... 123
7.4
Quantifying bias
......................... 123
7.5
Summary
............................. 125
Autocorrelation
127
8.1
Types
............................... 128
8.1.1
Independent identically distributed (IID)
....... 128
8.1.2
Moving average models (MA)
.............. 128
8.1.3
Autoregressive
models
(AR)............... 129
8.1.4
Self-similar series
(SSS)
................. 129
8.2
Characteristics
.......................... 130
8.2.1
Autocorrelation Function (ACF)
............ 130
8.2.2
The problems of autocorrelation
............ 136
8.3
Example: Testing statistical skill
................ 137
8.4
Within range
........................... 139
8.4.1
Beyond range
....................... 139
8.5
Generalization to 2D
...................... 140
8.6
Summary
............................. 141
Non-linearity
9.1
Growth niches
.......................... 144
9.1.1
Linear
........................... 145
9.1.2
Sigmoidal
......................... 145
9.1.3
Quadratic
......................... 147
9.1.4
Cubic
...........................
I54
9.2
Summary
............................. 155
10
Long term persistence
157
10.1
Detecting LTP
.......................... 159
10.1.1
Hurst Exponent
........<............. 162
10.1.2
Partial ACF
........................ 163
10.2
Implications of LTP
....................... 166
10.3
Discussion
............................ 171
11
Circularity
173
11.1
Climate prediction
........................ 173
11.1.1
Experiments
....................... 174
11.2
Lessons for niche modeling
................... 177
12
Fraud
179
12.1
Methods
............................. 181
12.1.1
Random numbers
..................... 181
12.1.2
CRU
............................ 184
12.1.3
Tree rings
......................... 186
12.1.4
Tidal Gauge
....................... 186
12.1.5
Tidal gauge
-
hand recorded
............... 188
12.2
Summary
............................. 190
References
191
Index
199
|
| adam_txt |
Contents
0.1
Preface
. xix
0.1.1
Summary of chapters
. xix
1
Functions
1
1.1
Elements
. 1
1.1.1
Factor
. 1
1.1.2
Complex
. 2
1.1.3
Raw
. 2
1.1.4
Vectors
. 2
1.1.5
Lists
. 3
1.1.6
Data frames
. 3
1.1.7
Time series
. 3
1.1.8
Matrix
. 4
1.2
Operations
. 4
1.3
Functions
. 6
1.4
Ecological models
. 9
1.4.1
Preferences
. 11
1.4.2
Stochastic functions
. 11
1.4.3
Random fields
. 18
1.5
Summary
. 21
2
Data
23
2.1
Creating
. 24
2.2
Entering data
. 25
2.3
Queries
. 26
2.4
Joins
. 28
2.5
Loading and saving a database
. 29
2.6
Summary
. 29
3
Spatial
31
3.1
Data types
. 31
3.2
Operations
. 34
3.2.1
Rasterizing
. 37
3.2.2
Overlay
. 37
3.2.3
Proximity
. 39
3.2.4
Cropping
. 40
3.2.5
Palette swapping
. 40
3.3
Summary
. 44
4
Topology
45
4.1
Formalism
. 45
4.2
Topology
. 47
4.3
Hutchinsonian niche
. 47
4.3.1
Species space
. 48
4.3.2
Environmental space
. 48
4.3.3
Topological generalizations
. 49
4.3.4
Geographic space
. 49
4.3.5
Relationships
. 50
4.4
Environmental envelope
. 51
4.4.1
Relevant variables
. 51
4.4.2
Tails of the distribution
. 51
4.4.3
Independence
. 52
4.5
Probability distribution
. 52
4.5.1
Dynamics
. 53
4.5.2
Generalized linear models
. 54
4.6
Machine learning methods
. 57
4.7
Data mining
. 58
4.7.1
Decision trees
. 59
4.7.2
Clustering
. 59
4.7.3
Comparison
. 59
4.8
Post-Hutchinsonian niche
. 60
4.8.1
Product space
. 61
4.9
Summary
. 63
5
Environmental data collections
65
5.1
Datasets
. 66
5.1.1
Global ecosystems database
. 88
5.1.2
Worldclim
. 89
5.1.3
World ocean atlas
. 90
5.1.4
Continuous fields
. 90
5.1.5
Hydrolkm
. 91
5.1.6
WhyWhere
. 91
5.2
Archives
. 91
5.2.1
Traffic
. 92
5.2.2
Management
. 92
5.2.3
Interaction
. 92
5.2.4
Updating
. 92
5.2.5
Legacy
. 92
5.2.6
Example: WhyWhere archive
. 93
5.2.7
Browsing
. 93
5.2.8
Format
. 94
5.2.9
Meta data
. 94
5.2.10
Operations
. 95
5.3
Summary
. 95
Examples
97
6.0.1
Model skill
. 97
6.0.2
Calculating accuracy
. 99
6.1
Predicting house prices
. 99
6.1.1
Analysis
. 100
6.1.2
Ρ
data and no mask
. 104
6.1.3
Presence and absence (PA) data
. 105
6.1.4
Interpretation
. 106
6.2
Brown Treesnake
. 107
6.2.1
Predictive model
. 107
6.3
Invasion of Zebra Mussel
. 109
6.4
Observations
. 113
Bias
115
7.1
Range shift
. 116
7.1.1
Example: climate change
. 116
7.2
Range-shift Model
. 117
7.3
Forms of bias
. 120
7.3.1
Width
r
and width error
. 120
7.3.2
Shift
s
and shift error
. 123
7.3.3
Proportional pe
. 123
7.4
Quantifying bias
. 123
7.5
Summary
. 125
Autocorrelation
127
8.1
Types
. 128
8.1.1
Independent identically distributed (IID)
. 128
8.1.2
Moving average models (MA)
. 128
8.1.3
Autoregressive
models
(AR). 129
8.1.4
Self-similar series
(SSS)
. 129
8.2
Characteristics
. 130
8.2.1
Autocorrelation Function (ACF)
. 130
8.2.2
The problems of autocorrelation
. 136
8.3
Example: Testing statistical skill
. 137
8.4
Within range
. 139
8.4.1
Beyond range
. 139
8.5
Generalization to 2D
. 140
8.6
Summary
. 141
Non-linearity
9.1
Growth niches
. 144
9.1.1
Linear
. 145
9.1.2
Sigmoidal
. 145
9.1.3
Quadratic
. 147
9.1.4
Cubic
.
I54
9.2
Summary
. 155
10
Long term persistence
157
10.1
Detecting LTP
. 159
10.1.1
Hurst Exponent
.<. 162
10.1.2
Partial ACF
. 163
10.2
Implications of LTP
. 166
10.3
Discussion
. 171
11
Circularity
173
11.1
Climate prediction
. 173
11.1.1
Experiments
. 174
11.2
Lessons for niche modeling
. 177
12
Fraud
179
12.1
Methods
. 181
12.1.1
Random numbers
. 181
12.1.2
CRU
. 184
12.1.3
Tree rings
. 186
12.1.4
Tidal Gauge
. 186
12.1.5
Tidal gauge
-
hand recorded
. 188
12.2
Summary
. 190
References
191
Index
199 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Stockwell, David Russell Bancroft |
| author_facet | Stockwell, David Russell Bancroft |
| author_role | aut |
| author_sort | Stockwell, David Russell Bancroft |
| author_variant | d r b s drb drbs |
| building | Verbundindex |
| bvnumber | BV023202328 |
| callnumber-first | Q - Science |
| callnumber-label | QH546 |
| callnumber-raw | QH546.3 |
| callnumber-search | QH546.3 |
| callnumber-sort | QH 3546.3 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WI 3060 |
| classification_tum | BIO 105f BIO 130f |
| ctrlnum | (OCoLC)255394095 (DE-599)GBV515676047 |
| dewey-full | 577.82 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 577 - Ecology |
| dewey-raw | 577.82 |
| dewey-search | 577.82 |
| dewey-sort | 3577.82 |
| dewey-tens | 570 - Biology |
| discipline | Biologie |
| discipline_str_mv | Biologie |
| format | Book |
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| genre | (DE-588)4173536-5 Patentschrift gnd-content |
| genre_facet | Patentschrift |
| id | DE-604.BV023202328 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:08:35Z |
| indexdate | 2024-07-09T21:12:57Z |
| institution | BVB |
| isbn | 1584884940 9781584884941 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016388535 |
| oclc_num | 255394095 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM DE-703 DE-83 |
| owner_facet | DE-M49 DE-BY-TUM DE-703 DE-83 |
| physical | 201 S. Ill., Kt., graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series2 | Chapman & Hall/CRC mathematical and computational biology series |
| spelling | Stockwell, David Russell Bancroft Verfasser aut Niche modeling predictions from statistical distributions David Stockwell Boca Raton Fla. [u.a.] Chapman & Hall/CRC 2007 201 S. Ill., Kt., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC mathematical and computational biology series Literaturverz. S. 191 - 197 Mathematisches Modell Niche (Ecology) Computer simulation Niche (Ecology) Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Ökologische Nische (DE-588)4172405-7 gnd rswk-swf (DE-588)4173536-5 Patentschrift gnd-content Ökologische Nische (DE-588)4172405-7 s Mathematisches Modell (DE-588)4114528-8 s DE-604 http://www.loc.gov/catdir/enhancements/fy0703/2006027353-d.html Publisher description lizenzfrei http://www.loc.gov/catdir/toc/ecip0619/2006027353.html lizenzfrei Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016388535&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Stockwell, David Russell Bancroft Niche modeling predictions from statistical distributions Mathematisches Modell Niche (Ecology) Computer simulation Niche (Ecology) Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd Ökologische Nische (DE-588)4172405-7 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4172405-7 (DE-588)4173536-5 |
| title | Niche modeling predictions from statistical distributions |
| title_auth | Niche modeling predictions from statistical distributions |
| title_exact_search | Niche modeling predictions from statistical distributions |
| title_exact_search_txtP | Niche modeling predictions from statistical distributions |
| title_full | Niche modeling predictions from statistical distributions David Stockwell |
| title_fullStr | Niche modeling predictions from statistical distributions David Stockwell |
| title_full_unstemmed | Niche modeling predictions from statistical distributions David Stockwell |
| title_short | Niche modeling |
| title_sort | niche modeling predictions from statistical distributions |
| title_sub | predictions from statistical distributions |
| topic | Mathematisches Modell Niche (Ecology) Computer simulation Niche (Ecology) Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd Ökologische Nische (DE-588)4172405-7 gnd |
| topic_facet | Mathematisches Modell Niche (Ecology) Computer simulation Niche (Ecology) Mathematical models Ökologische Nische Patentschrift |
| url | http://www.loc.gov/catdir/enhancements/fy0703/2006027353-d.html http://www.loc.gov/catdir/toc/ecip0619/2006027353.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016388535&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT stockwelldavidrussellbancroft nichemodelingpredictionsfromstatisticaldistributions |
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