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'Holey' niche! finding holes in niche hypervolumes using persistence homology.

ABSTRACT: Living organisms are limited in the range of values of ecological factors they can explore. This defines where animals exist (or could exist) and forms an ecological fingerprint that explains species' distribution at global scales. Species' ecological fingerprints can be represented as a n-dimensional hypervolume - known as Hutchinson's niche hypervolume. This concept has enabled significant progress in our understanding of species' ecological needs and distributions across environmental gradients. Nevertheless, the properties of Hutchinson's n-dimensional hypervolumes can be challenging to calculate and several methods have been proposed to extract meaningful measurements of hypervolumes' properties. One key property of hypervolumes are holes, which provide important information about the ecological occupancy of species. However, to date, current methods rely on volume estimates and set operations to identify holes in hypervolumes. Yet, this approach can be problematic because in high-dimensions, the volume of region enclosing a hole tends to zero. We propose the use of persistence homology (PH) to identify holes in hypervolumes and in ecological datasets more generally. PH allows for the estimates of topological properties in n-dimensional niche hypervolumes independent of the volume estimates of the hypervolume. We demonstrate the application of PH to canonical datasets and to the identification of holes in the hypervolumes of five vertebrate species with diverse niches, highlighting the potential benefits of this approach to gain further insights into animal ecology. Overall, our approach enables the study of a yet unexplored property of Hutchinson's hypervolumes, and thus, have important implications to our understanding of animal ecology.

SUBMITTER: Conceicao P

PROVIDER: S-EPMC9184459 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Understanding how species respond to the environment is essential in ecology, evolution, and conservation. Abiotic factors can influence species responses and the multi-dimensional space of abiotic factors that allows a species to grow represents the environmental niche. While niches are often assumed to be constant and robust, they are most likely changing over time and estimation can be influenced by population biology, sampling intensity, and computation methodology. Here, we used a 12-year time series of survey data to fit annual ecological niche models (ENMs) for 10 marine fish species by using two regression and two machine learning algorithms to evaluate the variation and differentiation of environmental niches. Fitted ENMs were used to develop multi-dimensional annual and pooled hypervolumes that were evaluated over time and across ENM algorithms, species, and years by computing volume, distance, and dissimilarity metrics for each annual estimated niche. We then investigated potential drivers of estimated hypervolume dynamics including species abundance, species occurrence, sampling effort, salinity, red tides severity, and algorithm. Overall, our results revealed that estimated niches varied over time and across ENM, species, and algorithms. Niche estimation was influenced over time by multiple factors suggesting high complexity on niche dynamics interpretation. Species with high occurrence tended to have a closer representation of the pooled niche and years with higher abundance tended to produce niche expansion. ENM algorithm, sampling effort, seawater salinity, and red tides explained the deviations from the pooled niche. Greater sampling effort led to more comprehensive and complete estimates of species niches. High red tides severity triggered niche contraction. Our results emphasize the predictable effects of population, sampling, and environment on species niche estimation and interpretation, and that each should be considered when performing and interpreting ecological niche analyses. Our niche analysis approach may contribute to effectively quantifying and assessing niche dynamics.

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'Holey' niche! finding holes in niche hypervolumes using persistence homology.

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