Hi evan;

As far as i know, to control biases of tracking and surveys (whatever the method used to obtain presence data) datasets is to become them spatially and temporarily equal. That is, using standardizing your datasets to make these datasets comparable across scales.

On the other hand, given the different nature of our datasets, you can use the R package named "biomod2" which integrates different modelling approaches, namely GLM, Maxent etc for the same datasets separately or combined.

I strongly recommend you to check this out, indeed this modelling approach is helpful to control biases instead of using solely one method.

See this paper; https://www.sciencedirect.com/science/article/pii/S1470160X15002393

I hope I've been of help to you. However, I think that other researchers with huge backaground in ecologuical modelling could give you more useful advises.

Best regards

Jon

Hi evan;

As far as i know, to control biases of tracking and surveys (whatever the method used to obtain presence data) datasets is to become them spatially and temporarily equal. That is, using standardizing your datasets to make these datasets comparable across scales.

On the other hand, given the different nature of our datasets, you can use the R package named "biomod2" which integrates different modelling approaches, namely GLM, Maxent etc for the same datasets separately or combined.

I strongly recommend you to check this out, indeed this modelling approach is helpful to control biases instead of using solely one method.

See this paper; https://www.sciencedirect.com/science/article/pii/S1470160X15002393

I hope I've been of help to you. However, I think that other researchers with huge backaground in ecologuical modelling could give you more useful advises.

Best regards

Jon

In addition to biomod2, as pointed out by Jon, you can also have a look at the r packages PresenceAbsence https://cran.r-project.org/web/packages/PresenceAbsence/PresenceAbsence.pdf, offering many different methods to test the model results, and spThin https://cran.r-project.org/web/packages/spThin/spThin.pdf allowing for spatial thinning.

Maybe this could help you:

https://github.com/BES-QSIG/Combining-different-data-types-in-a-single-model

You mean the Integrated SDMs described in Koshkina et al 2017?

If so, you can use the same R script published as supporting information.

It is free to download here:

https://besjournals.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F2041-210X.12738&file=mee312738-sup-0002-Supinfo.zip

I also recommend this paper:

Article The recent past and promising future for data integration me...

I hope this would help you,

Cheers!

Hi again evan;

I just saw that a couple of days ago a new MS was published which can be of great help to you.

Here is the URL of the preprint:

https://www.researchgate.net/publication/332612546_Combining_multiple_data_sources_in_species_distribution_models_while_accounting_for_spatial_dependence_and_overfitting_with_combined_penalised_likelihood_maximisation?_iepl%5BviewId%5D=ufvUAuJAWGixa0jM079XX2MK&_iepl%5BsingleItemViewId%5D=ZbRtbumdf6jCc5OJ0cNzRcVE&_iepl%5BpositionInFeed%5D=12&_iepl%5BhomeFeedVariantCode%5D=clst&_iepl%5BactivityId%5D=1098699451273216&_iepl%5BactivityType%5D=service_add_recommendation_activity&_iepl%5BactivityTimestamp%5D=1556391562&_iepl%5BrecTarActComb%5D=person_publish_publication&_iepl%5BrecActVar%5D=feed_via_people_you_follow_and_feed_interest_nodes_blended_signals_boost_preprints_and_reads>publications_via_interest_nodes_scored_by_keywords_and_cited_a_boost_preprints_and_reads&_iepl%5BrecScore%5D=675.66522216797&_iepl%5Bcontexts%5D%5B0%5D=homeFeed&_iepl%5BtargetEntityId%5D=PB%3A332612546&_iepl%5BinteractionType%5D=publicationTitle

Bests

Jon