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Ecography — Template for authors

Publisher: Wiley

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Guideline source

Categories	Rank	Trend in last 3 yrs
Ecology, Evolution, Behavior and Systematics	#18 of 647	up by 13 ranks

Journal quality:

High

Last 4 years overview: 636 Published Papers | 7179 Citations

Indexed in: Scopus

Last updated: 07/07/2020

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Insights

General info

Related Journals

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Journal of Marine Biology

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Quality:

High

CiteRatio: 3.1

SJR: 0.429

SNIP: 1.331

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Recommended

PLOS Computational Biology

PLOS

Quality:

High

CiteRatio: 7.3

SJR: 2.628

SNIP: 1.713

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PLOS Genetics

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Quality:

High

CiteRatio: 9.0

SJR: 3.587

SNIP: 1.457

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Oryx

Cambridge University Press

Quality:

High

CiteRatio: 4.2

SJR: 0.785

SNIP: 1.061

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.

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

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

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

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)

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.

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

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

Insights

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

Insights

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

Guideline source: View

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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 explaining contemporary ecological patterns. Manuscripts are expected to address general principles in ecology, though they may do so using a specific model system if this frames the problem relative to a generalised ecological issue. Papers in Ecography are expected to be based on concise hypotheses or to relate to concise theoretical concepts based on sound natural history. Purely descriptive papers are considered only if breaking new ground and/or describing patterns seldom explored. Papers are judged by virtue of their originality, appeal to general interest, and their contribution to new developments in ecological research concerning spatial and temporal patterns. There is no bias with regard to taxon, biome, or biogeographical area. Papers in the sections Forum and Software Notes are given high priority in the publication process. Ecography is a journal issued by the Nordic Ecological Society OIKOS, representing ecologists in Denmark, Finland, Iceland, Norway and Sweden. Ecologists from all countries are invited to publish original results on its pages. 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 explaining contemporary ecological patterns. Manuscripts are expected to address general principles in ecology, though they may do so using a specific model system if this frames the problem relative to a generalised ecological issue. Papers generally are expected to be based on concise hypotheses or to relate to concise theoretical concepts based on sound natural history. Purely descriptive papers are considered only if breaking new ground and/or describing patterns seldom explored. Manuscripts merely confirming or marginally extending results of previous work are discouraged. Papers are judged by virtue of their originality, appeal to general interest, and their contribution to new developments in ecological research concerning spatial and temporal patterns. There is no bias with regard to taxon, biome, or biogeographical area. Ecography publishes four types of articles:Original research papers present research focused on ecological and geographical patterns in space and time. Theoretically oriented papers are expected to synthesize and move beyond previous knowledge, preferably by suggesting new conceptual or methodological approaches to well-established problems. Among empirical studies, those that explore or test clearly stated hypotheses or theoretical predictions and attempt to generalize results to other systems are welcomed.Forum papers are commentaries, short papers or notes about new ideas, alternative ways of evaluating existing information or responses to recent papers in Ecography or elsewhere that deal with spatio-temporal issues in ecology. Notes focused on new or improved method are also welcome. Contributions in this category may be submitted or solicited.Review & Synthesis papers provide a critical assessment of the literature with emphasis on current topics in which rapid and significant advances are occurring. Ecography papers in this category should be more focused than the broad, topical reviews typically published elsewhere, developing a synthesis that inspires new hypotheses or new methods. Contributions in this category may be submitted or solicited.Software Notes announce new software or software already in use but not previously published in a peer-reviewed journal for the study of spatial and temporal patterns in ecology. Software Notes should provide a summary of the software that describes its benefits and potential application(s). Software Notes are published with high priority and the se Read Less

Ecology, Evolution, Behavior and Systematics

Agricultural and Biological Sciences

Last updated on	07 Jul 2020
ISSN	0906-7590
Impact Factor	High - 1.714
Open Access	Yes
Sherpa RoMEO Archiving Policy	Yellow
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	apa
Citation Type	Numbered [25]
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 access • Journal Article • DOI: 10.1111/J.2006.0906-7590.04596.X •

Novel methods improve prediction of species' distributions from occurrence data

Jane Elith¹, Catherine H. Graham², •

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

Topics:

Environmental niche modelling (57%)57% related to the paper

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7,589 Citations

Open 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

Carsten F. Dormann¹, Jane Elith¹, •

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

Topics:

Collinearity (68%)68% related to the paper

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6,199 Citations

Open 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 •

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

Topics:

Sampling (statistics) (53%)53% related to the paper

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5,314 Citations

Open 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

Carsten F. Dormann¹, Jana M. McPherson, •

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

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,

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2,820 Citations

Open access • Journal Article • DOI: 10.1111/J.1600-0587.2013.07872.X •

A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

Cory Merow¹, Cory Merow², •

01 Oct 2013 - Ecography

Abstract:

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy... The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions. read more

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2,370 Citations

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Ecography format uses apa citation style.

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Frequently asked questions

1. Can I write Ecography in LaTeX?

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

2. Do you follow the Ecography guidelines?

Yes, the template is compliant with the Ecography 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 Ecography?

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 Ecography citation style.

4. Can I use the Ecography 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 Ecography.

5. Can I use a manuscript in Ecography 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 Ecography that you can download at the end.

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

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

7. Where can I find the template for the Ecography?

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 Ecography'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 Ecography'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. Ecography an online tool or is there a desktop version?

SciSpace's Ecography 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 Ecography?

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 Ecography?”

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

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

12. Is Ecography'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 Ecography?

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 Ecography. 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:

Pre-prints as being the version of the paper before peer review and
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 Ecography?

The 5 most common citation types in order of usage for Ecography 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 Ecography?

16. Can I download Ecography 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 Ecography Endnote style according to Elsevier guidelines.

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Ecography — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

6.455

11.3

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

2.973

2.088

Insights

Insights

Ecography

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

Freedom from formatting guidelines

Easy support from all your favorite tools

Frequently asked questions

Fast and reliable, built for complaince.

No word template required

Verifed journal formats

Trusted by academicians

Fast and reliable,
built for complaince.