Incorporating ecological principles into statistical models for the prediction of species’ distribution and abundance

Abstract

Understanding the determinants of species' distributions and abundances is a central theme in ecology. The development of statistical models to achieve this has a long history and the notion that the model should closely reflect underlying scientific understanding has encouraged ecologists to adopt complex statistical methods as they arise. In this paper we describe a Bayesian hierarchical model that reflects a conceptual ecological model of multi-scaled environmental determinants of riverine fish species' distributions and abundances. We illustrate this with distribution and abundance data of a small-bodied fish species, the Empire gudgeon Hypseleotris galii, in the Mary and Albert Rivers, Queensland, Australia. Specifically, the model sought to address; 1) the extent that landscape-scale abiotic variables can explain the species' distribution compared to local-scale variables, 2) how local-scale abiotic variables can explain species' abundances, and 3) how are these local-scale relationships mediated by landscape-scale variables. Overall, the model accounted for around 60% of variation in the distribution and abundance of H. galii. The findings show that the landscape-scale variables explain much of the distribution of the species; however, there was considerable improvement in estimating the species' distribution with the addition of local-scale variables. There were many strong relationships between abundance and local-scale abiotic variables; however, several of these relationships were mediated by some of the landscape-scale variables. The extent of spatial autocorrelation in the data was relatively low compared to the distances among sampling reaches. Our findings exemplify that Bayesian statistical modelling provides a robust framework for statistical modelling that reflects our ecological understanding. This allows ecologists to address a range of ecological questions with a single unified probability model rather than a series of disconnected analyses.

A multidimensional typology of riverbank habitats explains the distribution of European grayling (Thymallus thymallus L.) fry in a temperate river

“…Such discrepancies (i.e., cross-scale interactions) stressed the confusion and possible misinterpretation, which may arise when analysing habitat use at multiple scales. In a hierarchical organisation, the range of variability of small-scale habitat variables is nested within that of larger-scale habitats, hence part of the biological variability observed at small scale is indeed accounted for by larger-scale variables, possibly resulting in cross-scale interactions (Stewart-Koster et al 2013). Scale-dependent patterns of habitat use by fish have also been reported in other studies.…”

“…Fish-habitat relationships have been explored at a variety of spatial scales, ranging from the microscale (about one metre), the mesoscale (tens of metres, e.g., riffle/pool successions) to the macroscale (hundreds of metres, e.g., comparison of river reaches across sites or of distinct habitats across a floodplain), each explaining part of the variability in fish abundance (Poizat & Pont 1996;Deschênes & Rodriguez 2007;Stewart-Koster et al 2013). Microhabitat investigations most often rely on point-sample measurements, where physical conditions are described near the fish (Moyle & Baltz 1985;Bovee et al 1998).…”

Temporal variability in hydrology modifies the influence of geomorphology on wetland distribution along a desert stream

“…Bendix & Hupp ; Hupp ; Górski & Buijse ; Stewart‐Koster et al . ; Valente, Latrubesse & Ferreira ). In those studies, hydrologic variability over time was reduced to an integrated value for each sampling site (e.g.…”

Accurate systematic frameworks are vital to advance ecological and evolutionary studies, with an example from Australian freshwater fish (Hypseleotris)

“…Much of this work, which has included various carp gudgeon species as well as many others, has indeed shown the prevalence of environmental, hydrological and habitat variables (as filters) in explaining both the distribution of particular species (Kennard et al 2007) and the distribution of life-history traits (Sternberg and Kennard 2013;. Stewart-Koster et al (2013) specifically consider firetail gudgeon and show the importance of both large-scale landscape and local-scale hydrological and habitat factors in understanding its realised distribution, which tends to be in the mid to upper catchment. Conversely, the accuracy of the model-based prediction of the presence of empire gudgeon in Rose et al (2016) is very high (95%), likely because of its specialised lowland habitat preferences.…”