One tMsPGOcc vs. Many msPGOcc: Model Choice and Sample Gaps
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Alexandria Shockney
May 7, 2026, 4:51:01 PM (3 days ago) May 7
to spOccupancy and spAbundance users
Hi All,
I am interested in learning more about choosing between a single multi-season, non-spatial occupancy model (tMsPGOcc) vs. running multiple single-season non-spatial occupancy models (msPGOcc) and comparing those results. In both instances, multiple species would be considered. I am interested in learning how species occupancy changes within my survey area by season. Of note, some species have uncertain seasonal presence across a year, and as such, I want to be careful in how I interpret occupancy model results and better understand which method is most appropriate for my system. I tried to design the survey in a way that increased certainty in population stability within a survey season by limiting each survey to ~4-6 weeks of time, however, there is some inherent uncertainty in stable presence between seasons.
I sampled 8 “transects” which were divided into multiple 1km sites (n=201). These sites were sampled across 4 seasons (Summer, Fall, Winter, Spring) and two years (2024-2025, 2025-2026). Each season had 2 replicates for each site, totaling 4 replicates for the entire dataset across two years of sampling. However, there are some notable gaps where not all sites were sampled each season or year because additional sites were added as our sampling capabilities grew.
8 Sampled Transects: 1A, 1B, 2A, 2B, 3A, 3B, 4A, and 4B. Each transect contains various quantities of sites (avg. ~25 sites per transect). Sampling gaps:
# 3A not sampled in Summer 2024
# 3B not sampled in Summer 2024, Winter 2025
# 4A/4B not sampled in Summer 2024, Fall 2024, Winter 2025, or Spring 2025 (Year 1)
# 2B only sampled for one replicate of Fall 2025
Given the data gaps, I feel compelled to analyze the data together in a single multi-species, multi-season, non-spatial occupancy model (tMsPGOcc) to increase the sample size and model performance. However, given the potential uncertainty in species presence across an annual cycle, I also feel the need to be cautious and perform 4 independent multi-species, single-season, non-spatial occupancy models (msPGOcc). Would performing 4 independent msPGOcc models yield similar results as a tMsPGOcc model? What cautious should be taken when comparing seasons that independently went through msPGOcc modeling? I saw a previous thread that had a similar question about accounting for seasonal trends in occupancy, and they were encouraged to consider “year” and “site_year” as random effects in the model (eg., "occ.formula = ~ (1 | year) + (1 | site_year) + ... would that not be appropriate in my instance because of potential species presence fluctuations between seasons?
Lastly, does anyone have recommendations on managing an occupancy dataset with a notable amount of missed repetitions? I had thought of considering "Season" as a repetition (ie., Summer = rep 1, 2; Fall = rep 3, 4; Winter = rep 5, 6; Summer = rep 7, 8) and condensing data across years, but would appreciate any insight on if there is a foundational issue with that approach when working with occupancy modeling.
I am quite new to occupancy modeling and am still trying to make sense of the best step forward to handle my dataset. Any help is much appreciated! If anyone needs any more information to help, please do not hesitate to ask!
With sincere gratitude,
Alexandria
I am interested in learning more about choosing between a single multi-season, non-spatial occupancy model (tMsPGOcc) vs. running multiple single-season non-spatial occupancy models (msPGOcc) and comparing those results. In both instances, multiple species would be considered. I am interested in learning how species occupancy changes within my survey area by season. Of note, some species have uncertain seasonal presence across a year, and as such, I want to be careful in how I interpret occupancy model results and better understand which method is most appropriate for my system. I tried to design the survey in a way that increased certainty in population stability within a survey season by limiting each survey to ~4-6 weeks of time, however, there is some inherent uncertainty in stable presence between seasons.
I sampled 8 “transects” which were divided into multiple 1km sites (n=201). These sites were sampled across 4 seasons (Summer, Fall, Winter, Spring) and two years (2024-2025, 2025-2026). Each season had 2 replicates for each site, totaling 4 replicates for the entire dataset across two years of sampling. However, there are some notable gaps where not all sites were sampled each season or year because additional sites were added as our sampling capabilities grew.
8 Sampled Transects: 1A, 1B, 2A, 2B, 3A, 3B, 4A, and 4B. Each transect contains various quantities of sites (avg. ~25 sites per transect). Sampling gaps:
# 3A not sampled in Summer 2024
# 3B not sampled in Summer 2024, Winter 2025
# 4A/4B not sampled in Summer 2024, Fall 2024, Winter 2025, or Spring 2025 (Year 1)
# 2B only sampled for one replicate of Fall 2025
Given the data gaps, I feel compelled to analyze the data together in a single multi-species, multi-season, non-spatial occupancy model (tMsPGOcc) to increase the sample size and model performance. However, given the potential uncertainty in species presence across an annual cycle, I also feel the need to be cautious and perform 4 independent multi-species, single-season, non-spatial occupancy models (msPGOcc). Would performing 4 independent msPGOcc models yield similar results as a tMsPGOcc model? What cautious should be taken when comparing seasons that independently went through msPGOcc modeling? I saw a previous thread that had a similar question about accounting for seasonal trends in occupancy, and they were encouraged to consider “year” and “site_year” as random effects in the model (eg., "occ.formula = ~ (1 | year) + (1 | site_year) + ... would that not be appropriate in my instance because of potential species presence fluctuations between seasons?
Lastly, does anyone have recommendations on managing an occupancy dataset with a notable amount of missed repetitions? I had thought of considering "Season" as a repetition (ie., Summer = rep 1, 2; Fall = rep 3, 4; Winter = rep 5, 6; Summer = rep 7, 8) and condensing data across years, but would appreciate any insight on if there is a foundational issue with that approach when working with occupancy modeling.
I am quite new to occupancy modeling and am still trying to make sense of the best step forward to handle my dataset. Any help is much appreciated! If anyone needs any more information to help, please do not hesitate to ask!
With sincere gratitude,
Alexandria
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