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I'm working with ebird data which looks like this:

Columns: 40
$ checklist_id                  <chr> "S31449436", "S6404935", "S6365577", "S6415673", "S6453069", "S652793...
$ observer_id                   <chr> "obs34276", "obs134751", "obs174363", "obs94083", "obs215920", "obs11...
$ sampling_event_identifier     <chr> "S31449436", "S6404935", "S6365577", "S6415673", "S6453069", "S652793...
$ scientific_name               <chr> "Melanerpes erythrocephalus", "Melanerpes erythrocephalus", "Melanerp...
$ observation_count             <dbl> 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ species_observed              <int> 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ state_code                    <chr> "US-NC", "US-SC", "US-MS", "US-GA", "US-FL", "US-FL", "US-NC", "US-SC...
$ locality_id                   <chr> "L878778", "L462851", "L658940", "L907862", "L127272", "L751567", "L8...
$ latitude                      <dbl> 35.93736, 32.79136, 32.32972, 31.26290, 29.91650, 30.54640, 36.01085,...
$ longitude                     <dbl> -76.51377, -80.19760, -90.14797, -81.97068, -82.58320, -85.36813, -75...
$ protocol_type                 <chr> "Stationary", "Traveling", "Traveling", "Stationary", "Traveling", "S...
$ all_species_reported          <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU...
$ observation_date              <date> 2016-09-06, 2010-05-12, 2010-05-04, 2010-05-14, 2010-05-23, 2010-05-...
$ year                          <dbl> 2016, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 201...
$ day_of_year                   <dbl> 250, 132, 124, 134, 143, 146, 146, 149, 177, 126, 126, 126, 130, 123,...
$ time_observations_started     <dbl> 8.716667, 8.500000, 8.250000, 12.666667, 9.000000, 6.750000, 8.583333...
$ duration_minutes              <dbl> 27, 240, 90, 20, 75, 3, 35, 210, 210, 30, 240, 25, 30, 20, 15, 210, 9...
$ effort_distance_km            <dbl> 0.000, 4.023, 0.644, 0.000, 1.609, 0.000, 0.805, 4.023, 4.828, 0.000,...
$ number_observers              <dbl> 1, 6, 2, 1, 1, 1, 1, 10, 6, 1, 3, 4, 1, 1, 1, 8, 1, 9, 1, 1, 1, 1, 1,...
$ pland_00_water                <dbl> 0.0937500, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1...
$ pland_01_evergreen_needleleaf <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.0000000...
$ pland_02_evergreen_broadleaf  <dbl> 0.00000000, 0.00000000, 0.00000000, 0.16666667, 0.40625000, 0.0000000...
$ pland_03_deciduous_needleleaf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ pland_04_deciduous_broadleaf  <dbl> 0.00000000, 0.29032258, 0.20000000, 0.13333333, 0.25000000, 0.0000000...
$ pland_05_mixed_forest         <dbl> 0.03125000, 0.35483871, 0.00000000, 0.00000000, 0.00000000, 0.0000000...
$ pland_06_closed_shrubland     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ pland_07_open_shrubland       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ pland_08_woody_savanna        <dbl> 0.34375000, 0.25806452, 0.16666667, 0.70000000, 0.34375000, 1.0000000...
$ pland_09_savanna              <dbl> 0.28125000, 0.00000000, 0.16666667, 0.00000000, 0.00000000, 0.0000000...
$ pland_10_grassland            <dbl> 0.09375000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.0000000...
$ pland_11_wetland              <dbl> 0.06250000, 0.03225806, 0.00000000, 0.00000000, 0.00000000, 0.0000000...
$ pland_12_cropland             <dbl> 0.03125000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.0000000...
$ pland_13_urban                <dbl> 0.00000000, 0.06451613, 0.46666667, 0.00000000, 0.00000000, 0.0000000...
$ pland_14_mosiac               <dbl> 0.0625000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0...
$ pland_15_barren               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ id                            <dbl> 1, 3, 5, 6, 7, 8, 9, 3, 3, 10, 11, 12, 13, 1, 1, 3, 3, 14, 3, 15, 16,...
$ elevation_median              <dbl> 5.280242, 7.022902, 95.591562, 20.789906, 15.495853, 71.982977, 4.564...
$ elevation_sd                  <dbl> 2.432494, 3.795833, 6.869224, 0.924082, 1.709597, 4.860347, 2.563131,...
$ index_median                  <dbl> 20266.44, 20316.16, 22763.83, 17957.14, 18702.73, 19674.35, 22298.44,...
$ index_sd                      <dbl> 1255.6209, 3023.5761, 1099.1277, 1189.0365, 809.8257, 1543.4811, 1491...

Where elevation_median and index_median or vegetation index, are the environmental variable, along with the different plands that are landcover types.

I want to find out appropriate statistical models that can help me answer these two questions:

  1. Analysing variation in abundance of a single species in space in relation to environmental variables
  2. Analysing variation in abundance of a single species in time - ie the trends in abundance, in relation to trends in environmental variable

As for background: The above code was created using the covariates page from ebird-best-practices https://cornelllabofornithology.github.io/ebird-best-practices/intro.html. I have followed the code that comes afterwards, i.e. encounter, occupancy and abundance. However, I am looking for new 'fresh' alternatives to approaching the questions above, that aren't included in the link above. Statistical complexity is not an issue as long as the data above is capable of being used.

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  • $\begingroup$ Can you add some details: Number of observations? Number of species? Approximate area? What kind of observations? eyesight? ringing? otherwise? Some explication of what is ebird data? $\endgroup$ – kjetil b halvorsen Oct 11 at 17:36
  • $\begingroup$ Currently, only working with a single species - Red-headed woodpecker, along the BCR region no. 27; Southeastern coastal plain. These observations are of bird counts, so if a birder saw a bird and correctly identified it, they would give it a count. protocol type is 'Stationary & Traveling', indicating whther they saw the bird whilst standing still or walking. The number of observations are dependent by the observation date, however, there are lots of zeros in the data. $\endgroup$ – Meilton Oct 11 at 17:48
  • $\begingroup$ There is a lot of information on this site: ebird.org/science/download-ebird-data-products $\endgroup$ – kjetil b halvorsen Oct 11 at 18:01
  • $\begingroup$ I have actually gone through all of it, especially the ebird-best-practices which is where the above code is partially made from. I was looking for new 'fresh-perspective' of alternative approaches to take towards the questions. $\endgroup$ – Meilton Oct 11 at 18:49
  • $\begingroup$ Can you please add this new information as an edit to the post? Not everybody reads comments ... $\endgroup$ – kjetil b halvorsen Oct 11 at 18:57

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