Shirota, S. | Search bioRxiv

bioRxiv2019

Clarifying species dependence under joint speciesdistribution modeling

O_LIJoint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These models attempt to capture species dependence through an associated correlation matrix arising from a set of latent multivariate normal variables. However, these associations offer little insight into dependence behavior between species at sites.\nC_LIO_LIWe focus on presence/absence data using joint species modeling which incorporates spatial dependence between sites. For pairs of species, we emphasize the induced odds ratios (along with the joint probabilities of occurrence); they provide much clearer understanding of joint presence/absence behavior. In fact, we propose a spatial odds ratio surface over the region of interest to capture how dependence varies over the region.\nC_LIO_LIWe illustrate with a dataset from the Cape Floristic Region of South Africa consisting of more than 600 species at more than 600 sites. We present the spatial distribution of odds ratios for pairs of species that are positively correlated and pairs that are negatively correlated under the joint species distribution model.\nC_LIO_LIThe multivariate normal covariance matrix associated with a collection of species is only a device for creating dependence among species but it lacks interpretation. By considering odds ratios, the quantitative ecologist will be able to better appreciate the practical dependence between species that is implicit in these joint species distribution modeling specifications.\nC_LI

ecology↗

bioRxiv2018

Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data

Presence/absence data and presence-only data are the two customary sources for learning about species distributions over a region. We present an ambitious agenda with regard to such data. We illuminate the fundamental modeling differences between the two types of data. Most simply, locations are considered as fixed under presence/absence data; locations are random under presence-only data. The definition of \"probability of presence\" is incompatible between the two. So, we take issue with modeling strategies in the literature which ignore this incom-patibility, which assume that presence/absence modeling can be induced from presence-only specifications and therefore, that fusion of presence-only and presence/absence data sources is routine.\n\nWe argue that presence/absence data should be modeled at point level. That is, we need to specify a surface which provides the probability of presence at any location in the region. A realization from this surface is a binary map yielding the results of Bernoulli trials across all locations; this surface is only partially observed. Presence-only data should be modeled as a (partially observed) point pattern, arising from a random number of individuals at random locations, driven by specification of an intensity function. There is no notion of Bernoulli trials; events are associated with areas.\n\nWe further argue that, with just presence/absence data, preferential sampling, using a shared process perspective, can improve our estimated presence/absence surface and prediction of presence. We also argue that preferential sampling can enable a probabilistically coherent fusion of the two data types.\n\nWe illustrate with two real datasets, one presence/absence, one presence-only for invasive species presence in New England in the United States. We demonstrate that potential bias in sampling locations can affect inference with regard to presence/absence and show that in-ference can be improved with preferential sampling ideas. We also provide a probabilistically coherent fusion of the two datasets to again improve inference with regard to presence/absence.\n\nThe importance of our work is to provide more careful modeling when studying species distributions. Ignoring incompatibility between data types and offering incoherent modeling specifications implies invalid inference; the community should benefit from this recognition.

ecology↗