  Species distribution modeling is a highly versatile tool for understanding
the intricate relationship between environmental conditions and species
occurrences. However, the available data often lacks information on confirmed
species absence and is limited to opportunistically sampled, presence-only
observations. To overcome this limitation, a common approach is to employ
pseudo-absences, which are specific geographic locations designated as negative
samples. While pseudo-absences are well-established for single-species
distribution models, their application in the context of multi-species neural
networks remains underexplored. Notably, the significant class imbalance
between species presences and pseudo-absences is often left unaddressed.
Moreover, the existence of different types of pseudo-absences (e.g., random and
target-group background points) adds complexity to the selection process.
Determining the optimal combination of pseudo-absences types is difficult and
depends on the characteristics of the data, particularly considering that
certain types of pseudo-absences can be used to mitigate geographic biases. In
this paper, we demonstrate that these challenges can be effectively tackled by
integrating pseudo-absences in the training of multi-species neural networks
through modifications to the loss function. This adjustment involves assigning
different weights to the distinct terms of the loss function, thereby
addressing both the class imbalance and the choice of pseudo-absence types.
Additionally, we propose a strategy to set these loss weights using spatial
block cross-validation with presence-only data. We evaluate our approach using
a benchmark dataset containing independent presence-absence data from six
different regions and report improved results when compared to competing
approaches.
