Example of Ecography format
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Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format
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Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography format Example of Ecography 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 ISSN: 9067590 e-ISSN: 16000587
recommended Recommended

Ecography — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Ecology, Evolution, Behavior and Systematics #18 of 647 up up by 13 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 636 Published Papers | 7179 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 07/07/2020
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor 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.

6.455

9% from 2018

Impact factor for Ecography from 2016 - 2019
Year Value
2019 6.455
2018 5.946
2017 4.52
2016 4.902
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.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

11.3

20% from 2019

CiteRatio for Ecography from 2016 - 2020
Year Value
2020 11.3
2019 9.4
2018 8.5
2017 8.3
2016 9.7
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 20% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

2.973

6% from 2019

SJR for Ecography from 2016 - 2020
Year Value
2020 2.973
2019 3.155
2018 3.049
2017 2.618
2016 3.552
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.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

2.088

3% from 2019

SNIP for Ecography from 2016 - 2020
Year Value
2020 2.088
2019 2.03
2018 1.905
2017 1.521
2016 2.023
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 3% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

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Hindawi

CiteRatio: 3.1 | SJR: 0.429 | SNIP: 1.331
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recommended Recommended

PLOS

CiteRatio: 7.3 | SJR: 2.628 | SNIP: 1.713
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PLOS

CiteRatio: 9.0 | SJR: 3.587 | SNIP: 1.457
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CiteRatio: 4.2 | SJR: 0.785 | SNIP: 1.061
Ecography

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Wiley

Ecography

Ecography publishes papers focused on broad spatial and temporal patterns, particularly studies of population and community ecology, macroecology, biogeography, and ecological conservation. Studies in ecological genetics and historical ecology are welcomed in the context of ex...... Read More

Ecology, Evolution, Behavior and Systematics

Agricultural and Biological Sciences

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Last updated on
07 Jul 2020
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ISSN
0906-7590
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Impact Factor
High - 1.714
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
Yellow faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1111/J.2006.0906-7590.04596.X
Novel methods improve prediction of species' distributions from occurrence data
01 Apr 2006 - Ecography

Abstract:

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for m... Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. read more read less

Topics:

Environmental niche modelling (57%)57% related to the paper
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6,718 Citations
open accessOpen access Journal Article DOI: 10.1111/J.1600-0587.2012.07348.X
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
01 Jan 2013 - Ecography

Abstract:

Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identificatio... Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them. read more read less

Topics:

Collinearity (68%)68% related to the paper
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4,522 Citations
open accessOpen access Journal Article DOI: 10.1111/J.0906-7590.2008.5203.X
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
Steven J. Phillips1, Miroslav Dudík
AT&T1
01 Apr 2008 - Ecography

Abstract:

Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, eve... Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use "default settings", tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presence-absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce "hinge features" that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore "background sampling" strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) "target-group" background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance. read more read less

Topics:

Sampling (statistics) (53%)53% related to the paper
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4,498 Citations
open accessOpen access Journal Article DOI: 10.1111/J.2007.0906-7590.05171.X
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review
01 Oct 2007 - Ecography

Abstract:

Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of ... Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method’s implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing read more read less

Topics:

Statistical model (58%)58% related to the paper, Spatial analysis (56%)56% related to the paper, Type I and type II errors (55%)55% related to the paper, Autocorrelation (53%)53% related to the paper, Estimating equations (53%)53% related to the paper
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2,535 Citations
open accessOpen access Journal Article DOI: 10.1111/J.0906-7590.2005.03957.X
Selecting thresholds of occurrence in the prediction of species distributions
Canran Liu1, Pam Berry, Terence P. Dawson, Richard G. Pearson
01 Jun 2005 - Ecography

Abstract:

Transforming the results of species distribution modelling from probabilities of or suitabilities for species occurrence to presences/absences needs a specific threshold. Even though there are many approaches to determining thresholds, there is no comparative study. In this paper, twelve approaches were compared using two spe... Transforming the results of species distribution modelling from probabilities of or suitabilities for species occurrence to presences/absences needs a specific threshold. Even though there are many approaches to determining thresholds, there is no comparative study. In this paper, twelve approaches were compared using two species in Europe and artificial neural networks, and the modelling results were assessed using four indices: sensitivity, specificity, overall prediction success and Cohen's kappa statistic. The results show that prevalence approach, average predicted probability/suitability approach, and three sensitivity-specificity-combined approaches, including sensitivity-specificity sum maximization approach, sensitivity-specificity equality approach and the approach based on the shortest distance to the top-left corner (0,1) in ROC plot, are the good ones. The commonly used kappa maximization approach is not as good as the afore-mentioned ones, and the fixed threshold approach is the worst one. We also recommend using datasets with prevalence of 50% to build models if possible since most optimization criteria might be satisfied or nearly satisfied at the same time, and therefore it's easier to find optimal thresholds in this situation. read more read less

Topics:

Cohen's kappa (53%)53% related to the paper, Maximization (50%)50% related to the paper
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1,938 Citations
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Ecography format uses apa citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Ecography guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Ecography citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Ecography's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

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 those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

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

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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Ecography.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Ecography Endnote style, according to wiley guidelines.

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