Building glmer poisson with repeated measures with random effects and interaction

Question

I have bird counts in three different landscape matrixes, repeated over two seasons. I want to evaluate if species richness differs between matrixes, accounting for seasonal changes.

I am not very familiar with building models using glmer, and I'm confused about how to consider the effect of the seasons, since points are repeated and then I need to consider this as a nested effect - but also I want to consider the possibility of an interaction of the effects of seasons and matrixes.

Some of my data:

birds<- data.frame(n = c(14L,1L,5L,2L,1L,4L,3L,3L,7L,3L,6L,5L,6L,1L,5L,1L,6L,5L,8L,11L,5L,10L,8L,5L,10L,2L,
                 5L,5L,2L,2L,2L,6L,5L,1L,13L,9L,9L,1L,12L,1L,1L,11L,3L,5L,5L,6L,3L,3L,1L,11L,2L,9L,7L,3L,11L,2L,
                 4L,8L,6L,5L,5L,1L,5L,14L,2L,3L,5L,5L,2L,2L,4L,3L,4L,2L,4L,6L,2L,1L,3L,5L,4L,1L,4L,3L,5L,2L,4L,
                 7L,2L,8L,5L,20L,3L,9L,8L,9L,3L,2L,9L,6L,4L,10L,7L,5L,6L,4L,4L,3L,2L,3L,5L,6L,5L,5L,12L,10L,7L,6L,
                 9L,2L,8L,6L,2L,19L,6L,7L,3L,5L,4L,7L,5L,7L,3L,2L,2L,1L,4L,4L,3L,5L,3L,6L,5L,4L,3L,3L,2L,4L,4L,
                 3L,2L,5L,2L,3L,3L,5L,3L,10L,6L,6L,6L,8L,13L,12L,6L,9L,5L,10L,6L,2L,5L,1L,15L,16L,10L,7L,7L,5L,8L,
                 6L,12L,3L,4L,8L,7L,10L,12L,10L,3L,4L,9L,6L,9L,10L,10L,6L,6L,9L,3L,10L,2L,10L,2L,4L,3L,15L,1L,5L,
                 1L,7L,11L,6L,3L,8L,5L,7L,10L,17L,5L,7L,9L,6L,10L,6L,2L,7L,2L,11L,6L,10L,4L,10L,7L,11L,5L,4L,1L,
                 2L,11L,8L,16L,5L,8L,8L,1L,7L,7L,1L,1L,10L,7L,4L,5L,1L,7L,1L,5L,6L,3L,1L,3L,10L,4L,7L,4L,1L,7L,1L,
                 6L,11L,5L,3L,4L,2L,7L,6L,3L,5L,2L,2L,7L,3L,8L,3L,4L),
       point = as.factor(c("Bo_01","Bo_01","Bo_02","Bo_02","Bo_03","Bo_03","Bo_04","Bo_04",
                  "Bo_05","Bo_05","Bo_06","Bo_06","Bo_07","Bo_07","Bo_08","Bo_08","Bo_09","Bo_09","Bo_10","Bo_10",
                  "Bo_11","Bo_11","Bo_12","Bo_12","Bo_13","Bo_13","Bo_14","Bo_14","Bo_15","Bo_15","Bo_16","Bo_16",
                  "Bo_17","Bo_17","Bo_18","Bo_18","Bo_19","Bo_19","Bo_20","Bo_20","Bo_21","Bo_21","Bo_22","Bo_22",
                  "Bo_23","Bo_23","Bo_24","Bo_24","Bo_25","Bo_25","Bo_26","Bo_26","Bo_27","Bo_27","Bo_28",
                  "Bo_28","Bo_29","Bo_29","Bo_30","Bo_30","Bo_31","Bo_31","Bo_32","Bo_32","Bo_33","Bo_33","Bo_34",
                  "Bo_34","Bo_35","Bo_35","Bo_36","Bo_36","Bo_37","Bo_37","Bo_38","Bo_38","Bo_39","Bo_39","Bo_40",
                  "Bo_40","Bo_41","Bo_41","Bo_42","Bo_42","Bo_43","Bo_43","Bo_44","Bo_44","Bo_45","Bo_45","Bo_46",
                  "Bo_46","Bo_47","Bo_47","Bo_48","He_01","He_01","He_02","He_02","He_03","He_03","He_04",
                  "He_04","He_05","He_05","He_06","He_06","He_07","He_07","He_08","He_08","He_09","He_09","He_10",
                  "He_10","He_11","He_11","He_12","He_12","He_13","He_13","He_14","He_14","He_15","He_15","He_16",
                  "He_16","He_17","He_17","He_18","He_18","He_19","He_19","He_20","He_20","He_21","He_21","He_22",
                  "He_22","He_23","He_23","He_24","He_24","He_25","He_25","He_26","He_26","He_27","He_27","He_28",
                  "He_28","He_29","He_29","He_30","He_30","He_31","He_31","He_32","He_32","He_33","He_33",
                  "He_34","He_34","He_35","He_35","He_36","He_36","He_37","He_37","He_38","He_38","He_39","He_39",
                  "He_40","He_40","He_41","He_41","He_42","He_42","He_43","He_43","He_44","He_44","He_45","He_45",
                  "He_46","He_46","He_47","He_47","He_48","Ho_01","Ho_01","Ho_02","Ho_02","Ho_03","Ho_03","Ho_04",
                  "Ho_04","Ho_05","Ho_05","Ho_06","Ho_06","Ho_07","Ho_07","Ho_08","Ho_08","Ho_09","Ho_09",
                  "Ho_10","Ho_10","Ho_11","Ho_11","Ho_12","Ho_12","Ho_13","Ho_13","Ho_14","Ho_14","Ho_15","Ho_15",
                  "Ho_16","Ho_16","Ho_17","Ho_17","Ho_18","Ho_18","Ho_19","Ho_19","Ho_20","Ho_20","Ho_21","Ho_21",
                  "Ho_22","Ho_22","Ho_23","Ho_23","Ho_24","Ho_24","Ho_25","Ho_25","Ho_26","Ho_26","Ho_27","Ho_27",
                  "Ho_28","Ho_28","Ho_29","Ho_29","Ho_30","Ho_30","Ho_31","Ho_31","Ho_32","Ho_32","Ho_33","Ho_33",
                  "Ho_34","Ho_34","Ho_35","Ho_35","Ho_36","Ho_36","Ho_37","Ho_37","Ho_38","Ho_38","Ho_39",
                  "Ho_39","Ho_40","Ho_40","Ho_41","Ho_41","Ho_42","Ho_42","Ho_43","Ho_43","Ho_44","Ho_44","Ho_45",
                  "Ho_45","Ho_46","Ho_46","Ho_47","Ho_47","Ho_48")),
      season = as.factor(c("D","R","D","R","D","R","D","R","D","R","D","R","D","R","D",
                  "R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D",
                  "R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","D","R","D","R","D",
                  "R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D",
                  "R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D",
                  "R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D",
                  "R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R","D","R",
                  "D","R","D","R","D","R","D","R","D","R","D","R","D","R","D")),
      matrix = as.factor(c("fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors","fors",
                 "fors","fors","fors","fors","fors","fors","het","het","het","het","het","het","het","het",
                 "het","het","het","het","het","het","het","het","het","het","het","het","het","het","het",
                 "het","het","het","het","het","het","het","het","het","het","het","het","het","het","het",
                 "het","het","het","het","het","het","het","het","het","het","het","het","het","het","het","het",
                 "het","het","het","het","het","het","het","het","het","het","het","het","het","het","het",
                 "het","het","het","het","het","het","het","het","het","het","het","het","het","het","het",
                 "het","het","het","het","het","het","het","het","het","het","het","hom","hom","hom","hom",
                 "hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom",
                 "hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom",
                 "hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom",
                 "hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom",
                 "hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom",
                 "hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom","hom")))

My guess is that I need to write a model with varying intercepts and slopes, with an interaction term.

For example:

In this model I understand I'm accounting for points within seasons and evaluating the effect of the matrix. But I'm not sure how to include an interaction term of season and matrix - any help on this?

m1<-glmer(n ~ matrix + (1|season), data = birds, family = poisson)

I also get a message: boundary (singular) fit: see ?isSingular and a variance zero for seasons effect, so I guess it is not a good idea to consider season as Random effect? How should I consider the seasons?

I reviewed different questions and found some other posts about repeated measures, modeling this as:

m2 <- glmer(n ~ season + matrix + season:matrix + (season | point), data = birds, family = poisson)

But I'm not sure that is correct in my example, since I get an error

Error: number of observations (=285) < number of random effects (=288) for term (season | point); the random-effects parameters are probably unidentifiable

Any help on how to specify my model correctly?