scholarly journals Capturing biodiversity complexities while accounting for imperfect detection: the application of occupancy-based diversity profiles

2020 ◽  
Author(s):  
Jesse F. Abrams ◽  
Rahel Sollmann ◽  
Simon L. Mitchell ◽  
Matthew J. Struebig ◽  
Andreas Wilting
Keyword(s):  
Species Abundance ◽  
Bird Community ◽  
Diversity Indices ◽  
Community Diversity ◽  
List Type ◽  
Occupancy Models ◽  
Imperfect Detection ◽  
Incidence Data ◽  
Hill Numbers ◽  
Diversity Profiles

AbstractMeasuring the multidimensional diversity properties of a community is of great importance for ecologists, conservationists and stakeholders. Diversity profiles, a plotted series of Hill numbers, simultaneously capture all the common diversity indices. However, diversity metrics require information on species abundance. They often rely on raw count data without accounting for imperfect and varying detection, although detectability can vary between species and study sites. Hierarchical occupancy models explicitly account for variation in detectability, and Hill numbers have been expanded to allow estimation based on occupancy probability. But agreement between occupancy and abundance-based diversity profiles has not been investigated.Here, we fit community occupancy models to simulated animal communities to explore how well occupancy-based diversity profiles reflect true abundance-based diversity. Because we expect occupancy-based diversity to be overestimated, we further tested a novel occupancy thresholding approach to reduce potential biases in the estimated diversity profiles. Finally, we use empirical data from a megadiverse bird community to present how the framework can be extended to consider trait or phylogeny-based similarity when calculating diversity profiles.The simulation study showed that occupancy-based diversity profiles produced among-community patterns in diversity similar to true abundance diversity profiles, although within-community diversity was overestimated with the exception of richness. While applying an occupancy threshold reduced this positive bias, this resulted in negative bias in species richness estimates and slightly reduced the ability to reproduce true differences among the simulated communities. Application of our approach to a large bird dataset revealed differential diversity patterns in communities of different habitat types. Accounting for phylogenetic and ecological similarities between species reduced diversity and its variability among habitats.Our framework allows investigating the complexity of diversity for incidence data, while accounting for imperfect and varying detection probabilities, as well as species similarities. Visualizing results in the form of diversity profiles facilitates comparison of diversity between sites or across time. Therefore, our extension to the diversity profile framework will be a useful tool for studying and monitoring biodiversity.

Combining counts and incidence data: an efficient approach for estimating the log-normal species abundance distribution and diversity indices

Oecologia ◽  
2012 ◽  
Vol 170 (2) ◽  
pp. 477-488 ◽  
Author(s):  
Edwige Bellier ◽  
Vidar Grøtan ◽  
Steinar Engen ◽  
Ann Kristin Schartau ◽  
Ola H. Diserud ◽  
...  
Keyword(s):  
Species Abundance ◽  
Diversity Indices ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Efficient Approach ◽  
Incidence Data ◽  
Log Normal

scholarly journals Information-based summary statistics for spatial genetic structure inference

2021 ◽  
Author(s):  
Xinghu Qin ◽  
Oscar E. Gaggiotti
Keyword(s):  
Genetic Structure ◽  
Spatial Structure ◽  
Spatial Genetic Structure ◽  
Diversity Indices ◽  
Discriminatory Power ◽  
List Type ◽  
Summary Statistics ◽  
Fisher Discriminant Analysis ◽  
Local Fisher Discriminant Analysis ◽  
Hill Numbers

AbstractInference of spatial patterns of genetic structure often relies on parameter estimation and model evaluation using a set of summary statistics (SS) that summarise the information present in the data. An important subset of these SS is best described as diversity indices, which are based on information theory principles that can be classified as belonging to three different ‘families’ encompassing a spectrum of information measures, qH. These include the richness family of order q = 0, ArSS; the Shannon family of order q = 1, HSS; and the heterozygosity family of order q = 2, HeSS. Although commonly used by ecologists, the Shannon family has been rather neglected by population geneticists and evolutionary biologists. However, recent population genetic studies have advocated their use, yet the power of these SS for spatial structure discrimination has not been systematically assessed.In this study, we performed a comprehensive assessment of the three families of SS, as well as a fourth family consisting of SS belonging to the Shannon family but expressed in terms of Hill numbers , for spatial structure inference using simulated microsatellites data under typical spatial scenarios. To give an unbiased evaluation, we used three machine learning methods, Kernel Local Fisher discriminant analysis (KLFDA), random forest classification (RFC), and deep neural network (DL), to test the performance of different SS to discriminate between spatial scenarios, and then identified the most informative metrics for discriminatory power.Results showed that the SS family of order q = 1 expressed in terms of Hill numbers, , outperformed the other two families (ArSS, HeSS) as well as the untransformed Shannon entropy (HSS) family. Jaccard dissimilarity (J) and its Mantel’s r showed the highest discriminatory power to discriminate all spatial scenarios, followed by Shannon differentiation ΔD and its Mantel’s r.Information-based summary statistics, especially the diversity of order q = 1 and Shannon differentiation measures, can increase the power of spatial structure inference. In addition, different sets of SS provide complementary power for discriminating between spatial scenarios.

scholarly journals Can Habitat Quality Index Measured Using the InVEST Model Explain Variations in Bird Diversity in an Urban Area?

2021 ◽  
Vol 13 (10) ◽  
pp. 5747
Author(s):  
Dehuan Li ◽  
Wei Sun ◽  
Fan Xia ◽  
Yixuan Yang ◽  
Yujing Xie
Keyword(s):  
Habitat Quality ◽  
Quality Index ◽  
Empirical Studies ◽  
Species Abundance ◽  
Diversity Indices ◽  
Local Scale ◽  
Data Availability ◽  
Bird Diversity ◽  
Invest Model ◽  
Trade Offs

Biodiversity maintenance is a crucial ecosystem service. Due to time limits and data availability, assessing biodiversity using indicators or models has become a hot topic in recent decades. However, whether some proposed indicators can explain biodiversity well at the local scale is still unclear. This study attempted to test whether the habitat quality index (HQI) as measured using the integrated valuation of ecosystem services and trade-offs (InVEST) model could explain variations in bird diversity in New Jiangwan Town, a rapidly urbanized region of Shanghai, China. The relationships from 2002 to 2013 among HQI and the two diversity indices, species richness and species abundance, were analyzed using Fisher’s exact test and gray correlation analysis. No significant association was found. Habitat connectivity was then integrated to develop a new combined indicator of habitat quality and connectivity index (HQCI). The associations between HQCI and the two diversity indices were improved significantly. The results indicated that connectivity may be an important factor explaining the diversity of certain species at a local scale. More empirical studies should be conducted to provide scientific evidence relating habitat quality to biodiversity.

scholarly journals Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys

2016 ◽  
Vol 3 (10) ◽  
pp. 160368 ◽  
Author(s):  
Campbell Murn ◽  
Graham J. Holloway
Keyword(s):  
Detection Probability ◽  
Spatial Scales ◽  
Survey Methods ◽  
A Priori ◽  
Low Density ◽  
Occupancy Models ◽  
Imperfect Detection ◽  
Factors Affecting ◽  
Sources Of Variation ◽  
Time Of Year

Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.

Influence of cliffs on bird community diversity

1998 ◽  
Vol 76 (2) ◽  
pp. 278-287 ◽  
Author(s):  
J D Matheson ◽  
D W Larson
Keyword(s):  
Bird Species ◽  
Bird Community ◽  
Community Diversity ◽  
Bird Species Richness ◽  
Narrow Component ◽  
Habitat Features ◽  
Habitat Mosaic ◽  
The North ◽  
Talus Slope ◽  
Additive Influence

Cliffs along the Niagara Escarpment in Ontario, Canada, support a long, narrow presettlement forest that includes three distinct geomorphic and vegetation zones: cliff edge, cliff face, and talus slope. This unique landform provides an opportunity to evaluate differences in bird communities between the escarpment and adjacent forest relative to habitat features. We sampled forest birds 12 times during the summer of 1994 in plots located in plateau forests, on talus slope, at cliff edges, and on cliff faces. Eleven habitat variables considered important to birds were also sampled in the plots. We arranged plots along six randomly spaced transects at a south site and a north site. Both sites had the consistent habitat heterogeneity considered important to birds. Bird species richness and composition responded to this heterogeneity, but differently at each site: plateau deciduous forests always had the lowest richness and the simplest species composition, whereas both cliff edges and talus slopes had a higher diversity of birds. Cliff faces had large numbers of species in the south but smaller numbers in the north. Escarpment zones form a habitat mosaic that supports many species not found in the adjacent forest and is consistent with the effect of habitat edge. The results suggest that cliffs represent a significant additive influence on avian biodiversity, even when the cliff is a very narrow component of the landscape.

scholarly journals Distance Effects on Diversity and Abundance of the Flower Visitors of Ocimum kilimandscharicum in the Kakamega Forest Ecosystem

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hellen K. Mandela ◽  
Mugatsia H. Tsingalia ◽  
Mary Gikungu ◽  
Wilbur M. Lwande
Keyword(s):  
Species Diversity ◽  
Diversity Index ◽  
Land Use Changes ◽  
Species Abundance ◽  
Diversity Indices ◽  
Forest Edge ◽  
Flower Visitors ◽  
Kakamega Forest ◽  
Species Invasions ◽  
Wiener Diversity Index

Pollination is an important ecosystem service in the maintenance of biodiversity and most importantly in food production. Pollination is on the decline due to habitat loss, exotic species invasions, pollution, overharvesting, and land use changes. This study analyzed the abundance and diversity of flower visitors’ of Ocimum kilimandscharicum in Kakamega forest with increasing distance from the forest edge. Data were collected through direct observation and sweep netting. Six study sites were identified along two transects each 2.5 km long and labeled A to F. Distance in metres from the forest edge to each site was A=221, B=72, C=83, D=198, E=113, and F=50. Sampling was done from 7:30 am to 4:00 pm, three days in a week for five months consecutively. Diversity indices of different flower visitors were calculated using the Shannon-Wiener diversity index. One-way analysis of variance was used to compare differences between sites and a two-sample t-test was used to identify mean significant differences in species diversity between the closest and the furthest sites. A total of 645 individuals belonging to 35 species were captured from 4 families. The highest diversity was at site F (H’= 2.38) which was closest to the forest edge and the lowest diversity was from site A (H’=1.44) which was furthest from the forest edge. Distance from the forest edge significantly influenced species diversity (F(3, 20)=14.67, p=0.024). Distance from the forest edge also significantly influenced species abundance between the furthest sites A, D, and E and the nearest sites F, B, and C to the forest edge (t=4.177; p=0.0312) and species richness (t=3.2893; p=0.0187). This study clearly demonstrates that Ocimum kilimandscharicum flower visitors play essential roles in pollination and their higher number of visits translates into higher numbers of seeds set. Many of these pollinators are associated with the forest and hence the need to conserve the Kakamega forest as a source pool for pollinators.

scholarly journals Combining single- and repeated-visit occupancy models to make the best of monitoring surveys

2019 ◽  
Author(s):  
Valentin Lauret ◽  
Hélène Labach ◽  
Matthieu Authier ◽  
Olivier Gimenez
Keyword(s):  
Bottlenose Dolphins ◽  
Ecological Monitoring ◽  
Western Mediterranean ◽  
List Type ◽  
Occupancy Models ◽  
Single Visit ◽  
The North ◽  
Aerial Surveys ◽  
Single Method ◽  
Repeated Visit

AbstractA major challenge in applied ecology consists in integrating knowledge from different datasets to produce robust ecological indicators. To estimate species distribution, occupancy models are a flexible framework that can accommodate several datasets obtained from different sampling methods. However, repeating visits at sampling sites is a prerequisite for using standard occupancy models, which may limit their use. Under certain conditions, detection/non-detection data collected during single visit can be analysed with occupancy models. To date however, single-visit occupancy models have never been used to combine several different datasets.Here, we developed an approach that combines multi-method and single-visit occupancy models. As a case study, we estimated the distribution of Bottlenose dolphins (Tursiops truncatus) over the North-western Mediterranean Sea by combining 24,624 km of aerial surveys and 21,464 km of at-sea monitoring. We compared the outputs of single-vs. repeated-visit multi-method occupancy models, and that of single-method occupancy models.Multi-method models allowed a better sampling coverage in both coasts and high seas and provided a better precision for occupancy estimates than single-method occupancy models using aerial surveys or at-sea surveys in isolation.Overall, single- and repeated-visit multi-method occupancy models produced similar inference about the distribution of bottlenose dolphins. This suggests that single-visit occupancy models provide robust occupancy estimates, which open promising perspectives for the use of non-standardized datasets.Synthesis and applications: Single-visit multi-method occupancy models can help making the best out of ecological monitoring programs by optimizing cost effectiveness through the formal combination of datasets.

scholarly journals A sparse observation model to quantify species interactions in time and space

2019 ◽  
Author(s):  
Sadoune Ait Kaci Azzou ◽  
Liam Singer ◽  
Thierry Aebischer ◽  
Madleina Caduff ◽  
Beat Wolf ◽  
...  
Keyword(s):  
Species Interactions ◽  
Activity Patterns ◽  
Niche Partitioning ◽  
Camera Trap ◽  
Camera Traps ◽  
Observation Model ◽  
Occupancy Models ◽  
Time And Space ◽  
Imperfect Detection ◽  
Mobile Species

SummaryCamera traps and acoustic recording devices are essential tools to quantify the distribution, abundance and behavior of mobile species. Varying detection probabilities among device locations must be accounted for when analyzing such data, which is generally done using occupancy models. We introduce a Bayesian Time-dependent Observation Model for Camera Trap data (Tomcat), suited to estimate relative event densities in space and time. Tomcat allows to learn about the environmental requirements and daily activity patterns of species while accounting for imperfect detection. It further implements a sparse model that deals well will a large number of potentially highly correlated environmental variables. By integrating both spatial and temporal information, we extend the notation of overlap coefficient between species to time and space to study niche partitioning. We illustrate the power of Tomcat through an application to camera trap data of eight sympatrically occurring duiker Cephalophinae species in the savanna - rainforest ecotone in the Central African Republic and show that most species pairs show little overlap. Exceptions are those for which one species is very rare, likely as a result of direct competition.

scholarly journals What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages?

2018 ◽  
Vol 35 ◽  
pp. 1-12
Author(s):  
Cynthia Diniz Souza ◽  
Vandick S. Batista ◽  
Nidia Noemi Fabré
Keyword(s):  
Wind Speed ◽  
Rainy Season ◽  
Fish Assemblages ◽  
Principal Component ◽  
Dry Season ◽  
Community Diversity ◽  
Tropical Species ◽  
Coastal Fish ◽  
Wide Range ◽  
Diversity Profiles

Seasonal ecological effects caused by temperature and photoperiod are typically considered minimal in the tropics. Nevertheless, annual climate cycles may still influence the distribution and abundance of tropical species. Here, we investigate whether seasonal patterns of precipitation and wind speed influence the structure of coastal fish assemblages and fishing yields in northeast Brazil. Research trips were conducted during the rainy and dry seasons using commercial boats and gear to sample the fish community. Diversity was analyzed using abundance Whittaker curves, diversity profiles and the Shannon index. Principal Component Analysis (PCA) was used to analyze associations between the abundance of species and various environmental variables related to seasonality. A total of 2,373 fish were collected, representing 73 species from 34 families – 20 of which were classified as both frequent and abundant. Species richness was greater and more equitable during the rainy season than the dry season – driven by changes in the precipitation rather than to wind speed. Species diversity profiles were slightly greater during the rainy season than the dry season, but this difference was not statistically significant. Using PCA was identified three groups of species: the first associated with wind speed, the second with precipitation, and the third with a wide range of sampling environments. This latter group was the largest and most ecologically heterogeneous. We conclude that tropical coastal fish assemblages are largely influenced by local variables, and seasonally mediated by annual changes related to precipitation intensity and wind speed, which in turn influences fishery yields.

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