Can models of presence‐absence be used to scale abundance? Two case studies considering extremes in life history

Abstract

Understanding patterns of species occurrence and abundance is a central theme of ecology, natural resource management, and conservation. Although occurrence models have been widely used for describing species distribution, particularly for rare species, abundance models are less common, despite greater information for conservation and management. Because presence-absence data are easier and less expensive to collect, predictions of abundance from patterns of occurrence could prove useful. We examined the relationship between occurrence and abundance for two species with very different life histories: bracken fern Pteridium aquilinum and moose Alces alces. We predicted that if occurrence and abundance were functionally related we should observe: 1) correlation between predicted probability of occurrence and observed abundance; 2) similar environmental covariates and estimated coefficients for occurrence models developed separately for low-density, high-density, and global data sites; and 3) parallel coefficients for the occurrence and abundance components of zero-inflated count models. Probability of occurrence was not correlated with abundance-when-present for bracken fern, while evidence for a relationship for moose was apparent at densities of animals below 7 individuals per cutblock. Coefficients for models at different levels of density did not vary significantly. However, once occurrence was accounted for, measured environmental data appeared less important in describing abundance. For bracken, covariates of zero-inflated count models differed in their expression of occurrence and abundance. Differences were less extreme for moose; however, results from the two-process models suggest that distribution and abundance may be a function of different processes. Environmental factors influencing abundance may differ from those limiting distribution. Life history, scale, site history, and sociocompetitive processes further help shape patterns of abundance. Two-stage modeling provides a powerful tool for describing animal and plant distribution where the processes of occurrence and abundance are influenced by different factors.

Does environmental suitability explain the relative abundance of the tailed tailless bat, Anoura caudifer?

“…A. caudifer is highly dependent of forested areas for feeding (Barquez & Diaz 2008), so it as hunting or deforestation (Tôrres et al 2012) that were not included in the model. Other variables such as site history, scale (grain), sampling errors in abundance estimates or species life history may also influence the spatial variation in local abundance (Nielsen et al 2005).…”

“…This also has practical implications for ecologists gathering biological data, since obtaining abundance data is much more costly and time consuming than obtaining presence data. However, it is worth mentioning that generalizations from suitability predicting abundance should be taken with caution, because we still do not have a sufficient number of tests about this subject and more studies are needed (Nielsen et al 2005;Jiménez-Valverde et al 2009;Thuiller et al 2010;Jiménez-Valverde 2011).…”

“…Therefore, testing if suitability can explain abundance may support or not if environmental suitability can be applied as a surrogate to the management and establishment of conservation units, helping conservationists to apply limited funding resources to species conservation. Then, it is important to test potential relationships between occurrence and abundance, examining where and when such relationships may hold true (Nielsen et al 2005).…”

“…can be considered as a habitat specialist. A homogenous resources distribution may reveal presence-absence patterns only, but it will not likely prove useful for modelling animal abundance (Nielsen et al 2005). Therefore, considering that A. caudifer is a habitat specialist and its feeding resources (nectar) are not homogeneously distributed, these factors may explain better why this relationship is stronger for it than for habitat generalist species (as the jaguar studied by Tôrres et al 2012).…”

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

“…This assumption is appropriate for many of the functional groups represented in Atlantis-GOM that are structure-oriented; the abundance of structure-oriented functional groups, such as red grouper and gag, may be primarily determined by the encounter/nonencounter of a suitable structure (Saul et al, 2013;Grüss et al, 2017c). Yet, spatial patterns of probability of encounter and spatial patterns of abundance often rely on slightly different determinants (Nielsen et al, 2005;Koubbi et al, 2006;Aarts et al, 2012;Grüss et al, 2014); thus, if we had produced distribution maps from abundance predictions (e.g., if we had had monitoring data for the entire GOM LME), some of those distribution maps may have been slightly different from the distribution maps constructed in the present study. In the future, if we had access to a reasonable amount of monitoring data for the southern GOM, it would be interesting to evaluate the consequences of employing abundance vs. probability of encounter maps to produce maps of relative abundance for Atlantis-GOM with our methodology.…”