Example of Ecological Informatics format
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Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format
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Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format Example of Ecological Informatics format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Ecological Informatics — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Applied Mathematics #58 of 548 down down by 16 ranks
Ecology, Evolution, Behavior and Systematics #100 of 647 up up by 10 ranks
Ecology #64 of 400 down down by 2 ranks
Modeling and Simulation #50 of 290 down down by 12 ranks
Computational Theory and Mathematics #26 of 133 down down by 6 ranks
Computer Science Applications #182 of 693 down down by 48 ranks
Ecological Modeling #9 of 32 down down by 1 rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 380 Published Papers | 1860 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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Related Journals

open access Open Access
recommended Recommended

PLOS

Quality:  
High
CiteRatio: 7.3
SJR: 2.628
SNIP: 1.713
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Springer

Quality:  
High
CiteRatio: 3.4
SJR: 0.291
SNIP: 0.951
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Springer

Quality:  
High
CiteRatio: 5.3
SJR: 1.515
SNIP: 0.684
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Elsevier

Quality:  
High
CiteRatio: 6.1
SJR: 1.882
SNIP: 1.743

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.511

9% from 2018

Impact factor for Ecological Informatics from 2016 - 2019
Year Value
2019 2.511
2018 2.31
2017 1.82
2016 2.02
graph view Graph view
table view Table view

4.9

11% from 2019

CiteRatio for Ecological Informatics from 2016 - 2020
Year Value
2020 4.9
2019 4.4
2018 4.3
2017 4.4
2016 3.5
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 9% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 11% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.774

6% from 2019

SJR for Ecological Informatics from 2016 - 2020
Year Value
2020 0.774
2019 0.825
2018 0.79
2017 0.778
2016 0.762
graph view Graph view
table view Table view

1.158

1% from 2019

SNIP for Ecological Informatics from 2016 - 2020
Year Value
2020 1.158
2019 1.151
2018 1.043
2017 1.045
2016 1.097
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 6% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 1% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Ecological Informatics

Guideline source: View

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Elsevier

Ecological Informatics

Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of ecoinformatics, computational ecology and systems ecology, and special issues on topics of current interest. The scope of the journal includes ecogenomics, informatio...... Read More

Mathematics

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Last updated on
09 Jun 2020
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ISSN
1574-9541
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Impact Factor
High - 1.354
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
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Citation Type
Numbered
[25]
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Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1016/J.ECOINF.2006.07.003
A novel numerical optimization algorithm inspired from weed colonization
Ali Reza Mehrabian1, Caro Lucas1
01 Dec 2006 - Ecological Informatics

Abstract:

This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environme... This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environment. Thus, capturing their properties would lead to a powerful optimization algorithm. It is tried to mimic robustness, adaptation and randomness of colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO). The feasibility, the efficiency and the effectiveness of IWO are tested in details through a set of benchmark multi-dimensional functions, of which global and local minima are known. The reported results are compared with other recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, and shuffled frog leaping. The results are also compared with different versions of simulated annealing — a generic probabilistic meta-algorithm for the global optimization problem — which are simplex simulated annealing, and direct search simulated annealing. Additionally, IWO is employed for finding a solution for an engineering problem, which is optimization and tuning of a robust controller. The experimental results suggest that results from IWO are better than results from other methods. In conclusion, the performance of IWO has a reasonable performance for all the test functions. read more read less

Topics:

Metaheuristic (62%)62% related to the paper, Simulated annealing (57%)57% related to the paper, Evolutionary algorithm (56%)56% related to the paper, Stochastic optimization (55%)55% related to the paper, Memetic algorithm (54%)54% related to the paper
999 Citations
Journal Article DOI: 10.1016/J.ECOINF.2010.12.003
A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors
Jin Li1, Andrew D. Heap1
01 Jul 2011 - Ecological Informatics

Abstract:

Spatial interpolation methods have been applied to many disciplines. Many factors affect the performance of the methods, but there are no consistent findings about their effects. In this study, we use comparative studies in environmental sciences to assess the performance and to quantify the impacts of data properties on the ... Spatial interpolation methods have been applied to many disciplines. Many factors affect the performance of the methods, but there are no consistent findings about their effects. In this study, we use comparative studies in environmental sciences to assess the performance and to quantify the impacts of data properties on the performance. Two new measures are proposed to compare the performance of the methods applied to variables with different units/scales. A total of 53 comparative studies were assessed and the performance of 72 methods/sub-methods compared is analysed. The impacts of sample density, data variation and sampling design on the estimations of 32 methods are quantified using data derived from their application to 80 variables. Inverse distance weighting (IDW), ordinary kriging (OK), and ordinary co-kriging (OCK) are the most frequently used methods. Data variation is a dominant impact factor and has significant effects on the performance of the methods. As the variation increases, the accuracy of all methods decreases and the magnitude of decrease is method dependent. Irregular-spaced sampling design might improve the accuracy of estimation. The effect of sampling density on the performance of the methods is found not to be significant. The implications of these findings are discussed. read more read less

Topics:

Sampling design (54%)54% related to the paper, Inverse distance weighting (52%)52% related to the paper, Multivariate interpolation (51%)51% related to the paper
560 Citations
Journal Article DOI: 10.1016/J.ECOINF.2013.11.002
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
Jan Beck1, Marianne Böller1, Andreas Erhardt1, Wolfgang Schwanghart1
01 Jan 2014 - Ecological Informatics

Abstract:

Species distribution modeling, in combination with databases of specimen distribution records, is advocated as a solution to the problem of distributional data limitation in biogeography and ecology. The global biodiversity information facility (GBIF), a portal that collates digitized collection and survey data, is the larges... Species distribution modeling, in combination with databases of specimen distribution records, is advocated as a solution to the problem of distributional data limitation in biogeography and ecology. The global biodiversity information facility (GBIF), a portal that collates digitized collection and survey data, is the largest online provider of distribution records. However, all distributional databases are spatially biassed due to uneven effort of sampling, data storage and mobilization. Such bias is particularly pronounced in GBIF, where nation-wide differences in funding and data sharing lead to huge differences in contribution to GBIF. We use a common Eurasian butterfly (Aglais urticae) as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF. Furthermore, we show that such loss of model quality would go unnoticed with standard methods of model quality evaluation. Using evaluations of model predictions of the Swiss distribution of the species, we compare distribution models of full data with data where a subsampling procedure removes spatial bias at the cost of record numbers, but not of spatial extent of records. We show that data with less spatial bias produce better predictive models even though they are based on less input data. Our subsampling routine may therefore be a suitable method to reduce the impact of spatial bias to species distribution models. Our results warn of automatized applications of species distribution models to distributional databases (as has been advocated and implemented), as internal model evaluation did not show the decline of model quality with increased spatial bias (but rather the opposite) while expert evaluation clearly did. read more read less
301 Citations
Journal Article DOI: 10.1016/J.ECOINF.2010.06.001
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges
01 Sep 2010 - Ecological Informatics

Abstract:

Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of ... Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time. read more read less

Topics:

Alpha diversity (62%)62% related to the paper, Beta diversity (61%)61% related to the paper, Species diversity (54%)54% related to the paper, Biodiversity (51%)51% related to the paper
View PDF
250 Citations
Journal Article DOI: 10.1016/J.ECOINF.2009.06.005
Automated classification of bird and amphibian calls using machine learning: A comparison of methods
01 Sep 2009 - Ecological Informatics

Abstract:

We compared the ability of three machine learning algorithms (linear discriminant analysis, decision tree, and support vector machines) to automate the classification of calls of nine frogs and three bird species In addition, we tested two ways of characterizing each call to train/test the system Calls were characterized with... We compared the ability of three machine learning algorithms (linear discriminant analysis, decision tree, and support vector machines) to automate the classification of calls of nine frogs and three bird species In addition, we tested two ways of characterizing each call to train/test the system Calls were characterized with four standard call variables (minimum and maximum frequencies, call duration and maximum power) or eleven variables that included three standard call variables (minimum and maximum frequencies, call duration) and a coarse representation of call structure (frequency of maximum power in eight segments of the call) A total of 10,061 isolated calls were used to train/test the system The average true positive rates for the three methods were: 9495% for support vector machine (094% average false positive rate), 8920% for decision tree (125% average false positive rate) and 7145% for linear discriminant analysis (198% average false positive rate) There was no statistical difference in classification accuracy based on 4 or 11 call variables, but this efficient data reduction technique in conjunction with the high classification accuracy of the SVM is a promising combination for automated species identification by sound By combining automated digital recording systems with our automated classification technique, we can greatly increase the temporal and spatial coverage of biodiversity data collection read more read less

Topics:

Call duration (58%)58% related to the paper, Linear discriminant analysis (55%)55% related to the paper, False positive rate (54%)54% related to the paper, Support vector machine (51%)51% related to the paper
234 Citations
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Ecological Informatics format uses elsarticle-num citation style.

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Frequently asked questions

1. Can I write Ecological Informatics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Ecological Informatics guidelines and auto format it.

2. Do you follow the Ecological Informatics guidelines?

Yes, the template is compliant with the Ecological Informatics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Ecological Informatics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Ecological Informatics citation style.

4. Can I use the Ecological Informatics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Ecological Informatics.

5. Can I use a manuscript in Ecological Informatics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Ecological Informatics that you can download at the end.

6. How long does it usually take you to format my papers in Ecological Informatics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Ecological Informatics.

7. Where can I find the template for the Ecological Informatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Ecological Informatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Ecological Informatics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Ecological Informatics an online tool or is there a desktop version?

SciSpace's Ecological Informatics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Ecological Informatics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Ecological Informatics?”

11. What is the output that I would get after using Ecological Informatics?

After writing your paper autoformatting in Ecological Informatics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Ecological Informatics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Ecological Informatics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Ecological Informatics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Ecological Informatics?

The 5 most common citation types in order of usage for Ecological Informatics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Ecological Informatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Ecological Informatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Ecological Informatics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Ecological Informatics Endnote style according to Elsevier guidelines.

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