Plant species assemblages can contain climate-related information, but it is unclear whether plant language models remain useful when observations are incomplete. We compare a sparse LightGBM baseline with Pl@ntBERT and a tropically adapted variant combining vocabulary expansion and domain-adaptive pretraining. The models predict four WorldClim variables from species composition in São Paulo state under progressively shortened species lists. The clearest pattern occurs for precipitation of the driest month (BIO14). On the original split, A2-MT shows positive differences over LightGBM under severe incompleteness after hierarchical bootstrap and multiplicity correction. Because BIO14 was selected post hoc, we retrain the models on five alternative assignments of a nominal 40 km spatial-block design. LightGBM is higher on complete lists in four of five partitions, while positive BIO14 differences under incomplete lists are directionally more stable but retain corrected support in only two partitions. Additional checks show that the pattern persists with a longer context window, weakens under frequency-weighted removal, and narrows to Max-10 and Max-5 when both model families receive truncation-augmented training. These results show that within-split robustness does not substitute for retraining across alternative spatial partitions.