The comparative analysis of species occurrence patterns on archipelagos

R. T. Ryti; M. E. Gilpin

doi:10.1007/bf00377519

Habitat destruction in wetland affects Ostracoda (Crustacea) species occurrence patterns amid different aquatic habitats

Aquatic Ecology ◽

10.1007/s10452-021-09934-6 ◽

2022 ◽

Author(s):

Okan Külköylüoğlu ◽

Beyza Çelikbaş ◽

Alper Ataman

Keyword(s):

Habitat Destruction ◽

Aquatic Habitats ◽

Species Occurrence ◽

Occurrence Patterns

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Soundscapes predict species occurrence in tropical forests

10.1101/2020.09.24.311381 ◽

2020 ◽

Author(s):

Sarab S. Sethi ◽

Robert M. Ewers ◽

Nick S. Jones ◽

Jani Sleutel ◽

Adi Shabrani ◽

...

Keyword(s):

Large Scale ◽

Area Under The Curve ◽

Forest Degradation ◽

Common Species ◽

List Type ◽

Species Occurrence ◽

Point Count ◽

Audio Data ◽

Taxonomic Groups ◽

Occurrence Patterns

AbstractAccurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious, and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys, but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g., for silent species). A new, intermediate approach is needed that rapidly predicts species occurrence without requiring extensive labelled data.We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species across a tropical forest degradation gradient in Sabah, Malaysia. We developed a machine-learning based approach to characterise species indicative soundscapes, training our models on a coarsely labelled manual point-count dataset.Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics of up to 0.87 (Bold-striped Tit-babbler Macronus bornensis). The highest accuracies were achieved for common species with strong temporal occurrence patterns.Soundscapes were a better predictor of species occurrence than above-ground biomass – a metric often used to quantify habitat quality across forest degradation gradients.Synthesis and applications: Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species. This provides a new direction for audio data to deliver large-scale, accurate assessments of habitat suitability using cheap and easily obtained field datasets.

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Occurrence Patterns, Photoperiod and Dispersion Ability of the Non-Marine Ostracoda (Crustacea) in Shallow Waters

Turkish Journal of Fisheries and Aquatic Sciences ◽

10.4194/1303-2712-v21_2_03 ◽

2020 ◽

Vol 21 (02) ◽

pp. 73-85

Author(s):

Okan Külköylüoğlu ◽

Mehmet Yavuzatmaca ◽

Derya Akdemir

Keyword(s):

Habitat Preferences ◽

Sampling Time ◽

Shallow Waters ◽

Species Occurrence ◽

Natural Habitats ◽

Effective Factor ◽

Species Pairs ◽

Occurrence Pattern ◽

Species Specific ◽

Occurrence Patterns

Patterns of species occurrence, dispersion ability, habitat preferences and sampling time can be important factors on the species composition. To understand effective roles of these factors on non-marine ostracods, samples were collected from 98 different shallow aquatic bodies from Osmaniye and Kilis provinces in Turkey. Total of 16 and 12 species were identified from the two provinces, respectively. All species are reported new for these provinces. Three species (Heterocypris incongruens, Ilyocypris inermis, I. bradyi) demonstrated the most frequent occurrences and abundances in up to seven different habitats. Species diversity and abundance were at least two times higher in natural habitats (streams, creeks) than artificial habitats (reservoirs, troughs). Numbers of species with and without swimming setae on the second antenna was not significantly different between lentic and lotic habitats. A positive co-occurrence pattern was found between Neglecandona neglecta and I. inermis while the rest of the species pairs exhibited random co-occurrences to each other. Canonical Correspondence Analysis showed 80.8% of correlation between species and environmental variables when water temperature was the major effective factor (P<0.05) on species occurrence. Sampling time did not make difference on the numbers of species between morning (06:30-11:58 a.m.) and after noon (12:05-19:52 p.m.). Results suggest that species occurrence seems to be related to species-specific characteristics in its n-dimensional niche where species deals with several other factors.

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Metadata description of the ORCA database (ORganic and Conventional Agriculture's impact on aquatic biodiversity)

Freshwater Metadata Journal ◽

10.15504/fmj.2021.52 ◽

2021 ◽

pp. 1-11

Author(s):

Marie Cours ◽

Pieter Lemmens ◽

Rafaela Almeida ◽

Rein Brys ◽

Luc Denys ◽

...

Keyword(s):

Comparative Analysis ◽

Species Occurrence ◽

Aquatic Biodiversity ◽

Physical Chemical ◽

Occurrence Data

The database of the ORCA project (A comparative analysis of ORganic and Conventional Agriculture's impact on aquatic biodiversity) comprises species occurrence data of different organism groups (zooplankton, macro-invertebrates, macrophytes, amphibians (eDNA) and fish (eDNA)) and data on physical, chemical and morphometric variables of 48 small farmland ponds distributed over Flanders, Belgium.

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A comment on priors for Bayesian occupancy models

10.1101/138735 ◽

2017 ◽

Author(s):

Joseph M. Northrup ◽

Brian D. Gerber

Keyword(s):

Bayesian Modeling ◽

Prior Information ◽

Sensitivity Analyses ◽

Occupancy Models ◽

Species Occurrence ◽

Imperfect Detection ◽

Data Set ◽

Online Tool ◽

Influence Parameter ◽

Occurrence Patterns

AbstractUnderstanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists often intend to choose priors so that they are “uninformative”; or “;vague”;, such priors can easily be unintentionally highly informative. Here we report on how the specification of a “vague” normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulation and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the occurrence of this issue likely depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or use priors other than those most commonly used in the literature. We provide further suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts.

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