Interpreting Ecological Data The Interpretation of Ecological Data: A Primer on Classification and Ordination E. C. Pielou

BioScience ◽  
1986 ◽  
Vol 36 (3) ◽  
pp. 205-206
Author(s):  
John H. Crow
Keyword(s):  
Ecological Data

Current distribution of Nilgiri grass yellow Eurema nilgiriensis Yata (Lepidoptera: Pieridae), with an updated taxonomic key to Eurema of Western Ghats, India

ENTOMON ◽  
2019 ◽  
Vol 44 (1) ◽  
pp. 23-32 ◽  
Author(s):  
P. C. Sujitha ◽  
G. Prasad ◽  
R. Nitin ◽  
Dipendra Nath Basu ◽  
Krushnamegh Kunte ◽  
...  
Keyword(s):  
Current Distribution ◽  
Western Ghats ◽  
Original Description ◽  
High Elevation ◽  
Type Locality ◽  
Ecological Data ◽  
Field Identification ◽  
Wing Patterns ◽  
Southern Western Ghats ◽  
The Nilgiris

Eurema nilgiriensis Yata, 1990, the Nilgiri grass yellow, was described from Nilgiris in southern India. There are not many published records of this species since its original description, and it was presumed to be a high-elevation endemic species restricted to its type locality. Based on the external morphology (wing patterns) as well as the male genitalia, the first confirmed records of the species from Agasthyamalais and Kodagu in the southern Western Ghats, is provided here. This report is a significant range extension for the species outside the Nilgiris, its type locality. Ecological data pertaining to this species as well as the field identification key to all known Eurema of Western Ghats are also presented.

scholarly journals U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2611
Author(s):  
Andrew Shepley ◽  
Greg Falzon ◽  
Christopher Lawson ◽  
Paul Meek ◽  
Paul Kwan
Keyword(s):  
Deep Learning ◽  
Intellectual Property ◽  
Object Detection ◽  
Image Data ◽  
Learning Technologies ◽  
Training Data ◽  
Learning Models ◽  
Ecological Data ◽  
Single Class ◽  
Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

scholarly journals Phylogenetic paleoecology: macroecology within an evolutionary framework

Paleobiology ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 171-177
Author(s):  
James C. Lamsdell ◽  
Curtis R. Congreve
Keyword(s):  
Species Diversity ◽  
Spatial Variation ◽  
Geographic Distribution ◽  
Evolutionary Biology ◽  
Phylogenetic Signal ◽  
Evolutionary Rates ◽  
The Other ◽  
Ecological Data ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial

The burgeoning field of phylogenetic paleoecology (Lamsdell et al. 2017) represents a synthesis of the related but differently focused fields of macroecology (Brown 1995) and macroevolution (Stanley 1975). Through a combination of the data and methods of both disciplines, phylogenetic paleoecology leverages phylogenetic theory and quantitative paleoecology to explain the temporal and spatial variation in species diversity, distribution, and disparity. Phylogenetic paleoecology is ideally situated to elucidate many fundamental issues in evolutionary biology, including the generation of new phenotypes and occupation of previously unexploited environments; the nature of relationships among character change, ecology, and evolutionary rates; determinants of the geographic distribution of species and clades; and the underlying phylogenetic signal of ecological selectivity in extinctions and radiations. This is because phylogenetic paleoecology explicitly recognizes and incorporates the quasi-independent nature of evolutionary and ecological data as expressed in the dual biological hierarchies (Eldredge and Salthe 1984; Congreve et al. 2018; Fig. 1), incorporating both as covarying factors rather than focusing on one and treating the other as error within the dataset.

scholarly journals Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors

2021 ◽  
Author(s):  
Pascual LÓPEZ-LÓPEZ ◽  
Arturo M PERONA ◽  
Olga EGEA-CASAS ◽  
Jon ETXEBARRIA MORANT ◽  
Vicente URIOS
Keyword(s):  
Energy Expenditure ◽  
Parental Care ◽  
Breeding Season ◽  
Video Recording ◽  
Experimental Manipulation ◽  
Biparental Care ◽  
Breeding Ecology ◽  
Parental Effort ◽  
Ecological Data ◽  
Movement Trajectories

Abstract Cutting-edge technologies are extremely useful to develop new workflows in studying ecological data, particularly to understand animal behaviour and movement trajectories at the individual level. Although parental care is a well-studied phenomenon, most studies have been focused on direct observational or video recording data, as well as experimental manipulation. Therefore, what happens out of our sight still remains unknown. Using high-frequency GPS/GSM dataloggers and tri-axial accelerometers we monitored 25 Bonelli’s eagles (Aquila fasciata) during the breeding season to understand parental activities from a broader perspective. We used recursive data, measured as number of visits and residence time, to reveal nest attendance patterns of biparental care with role specialization between sexes. Accelerometry data interpreted as the Overall Dynamic Body Acceleration, a proxy of energy expenditure, showed strong differences in parental effort throughout the breeding season and between sexes. Thereby, males increased substantially their energetic requirements, due to the increased workload, while females spent most of the time on the nest. Furthermore, during critical phases of the breeding season, a low percentage of suitable hunting spots in eagles’ territories led them to increase their ranging behaviour in order to find food, with important consequences in energy consumption and mortality risk. Our results highlight the crucial role of males in raptor species exhibiting biparental care. Finally, we exemplify how biologging technologies are an adequate and objective method to study parental care in raptors as well as to get deeper insight into breeding ecology of birds in general.

scholarly journals COVID-19 Pandemic Turns Life-Science Students into “Citizen Scientists”: Data Indicate Multiple Negative Effects of Urbanization on Biota

2021 ◽  
Vol 13 (5) ◽  
pp. 2992
Author(s):  
Jens Schirmel
Keyword(s):  
Citizen Science ◽  
Online Teaching ◽  
Teaching Strategy ◽  
Ecosystem Functions ◽  
Negative Effects ◽  
Ecological Data ◽  
Pollinating Insects ◽  
Impact Of Urbanization ◽  
Data Collections ◽  
The Impact

The COVID-19 pandemic and its restrictions strongly affect the higher education community and require diverse teaching strategies. We designed a course where we combined online teaching with independently conducted ecological data collections by students using a “citizen science” approach. The aim was to analyze the impact of urbanization on biota by comparing urban and rural grasslands. Seventy-five students successfully conducted the data collections and the results provide evidence for prevailing negative effects of urbanization. Individual numbers of ground-dwelling invertebrates (−25%) and pollinating insects (−33%) were generally lower in urban sites. Moreover, animal and seed predation were reduced in urban grasslands, indicating the potential of urbanization to alter ecosystem functions. Despite the general limitations of online teaching and citizen science approaches, outcomes of this course showed this combination can be a useful teaching strategy, which is why this approach could be used to more actively involve students in scientific research.

scholarly journals Geographic Structure of Mitochondrial and Nuclear Gene Polymorphisms in Australian Green Turtle Populations and Male-Biased Gene Flow

Genetics ◽  
1997 ◽  
Vol 147 (4) ◽  
pp. 1843-1854 ◽  
Author(s):  
Nancy N FitzSimmons ◽  
Craig Moritz ◽  
Colin J Limpus ◽  
Lisa Pope ◽  
Robert Prince
Keyword(s):  
Gene Flow ◽  
Genetic Structure ◽  
Green Turtle ◽  
Nuclear Dna ◽  
Nuclear Gene ◽  
Chelonia Mydas ◽  
Single Copy ◽  
Microsatellite Data ◽  
Ecological Data ◽  
Infinite Allele Model

Abstract The genetic structure of green turtle (Chelonia mydas) rookeries located around the Australian coast was assessed by (1) comparing the structure found within and among geographic regions, (2) comparing microsatellite loci vs. restriction fragment length polymorphism analyses of anonymous single copy nuclear DNA (ascnDNA) loci, and (3) comparing the structure found at nuclear DNA markers to that of previously analyzed mitochondrial (mtDNA) control region sequences. Significant genetic structure was observed over all regions at both sets of nuclear markers, though the microsatellite data provided greater resolution in identifying significant genetic differences in pairwise tests between regions. Inferences about population structure and migration rates from the microsatellite data varied depending on whether statistics were based on the stepwise mutation or infinite allele model, with the latter being more congruent with geography. Estimated rates of gene flow were generally higher than expected for nuclear DNA (nDNA) in comparison to mtDNA, and this difference was most pronounced in comparisons between the northern and southern Great Barrier Reef (GBR). The genetic data combined with results from physical tagging studies indicate that the lack of nuclear gene divergence through the GBR is likely due to the migration of sGBR turtles through the courtship area of the nGBR population, rather than male-biased dispersal. This example highlights the value of combining comparative studies of molecular variation with ecological data to infer population processes.

Long‐term hydrological, biogeochemical, and ecological data for the Kuparuk River, North Slope, Alaska

2021 ◽  
Author(s):  
Alexander B. Medvedeff ◽  
Frances M. Iannucci ◽  
Linda A. Deegan ◽  
Alexander D. Huryn ◽  
William B. Bowden
Keyword(s):  
North Slope ◽  
Ecological Data ◽  
Kuparuk River

Implementation of Self-Organizing Maps (SOM) to analyses of environmental parameters and phytoplankton biomass in a macrotidal estuary and artificial lake

2012 ◽  
Vol 93 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Roksana Jahan ◽  
Hyu Chang Choi ◽  
Young Seuk Park ◽  
Young Cheol Park ◽  
Ji Ho Seo ◽  
...  
Keyword(s):  
Phytoplankton Biomass ◽  
Algal Blooms ◽  
Suspended Solids ◽  
Inorganic Nitrogen ◽  
Environmental Parameters ◽  
Ecological Data ◽  
Self Organizing Maps ◽  
Macrotidal Estuary ◽  
Phytoplankton Communities ◽  
Self Organizing

Self-Organizing Maps (SOM) have been used for patterning and visualizing ten environmental parameters and phytoplankton biomass in a mactrotidal (>10 m) Gyeonggi Bay and artificial Shihwa Lake during 1986–2004. SOM segregated study areas into four groups and ten subgroups. Two strikingly alternative states are frequently observed: the first is a diverse non-eutrophic state designated by three groups (SOM 1–3), and the second is a eutrophic state (SOM 4: Shihwa Lake and Upper Gyeonggi Bay; summer season) characterized by enhanced nutrients (3 mg l−1 dissolved inorganic nitrogen, 0.1 mg l−1 PO4) that act as a signal and response to that signal as algal blooms (24 µg chlorophyll-a l−1). Bloom potential in response to nitrification is affiliated with high temperature (r = 0.26), low salinity (r = −0.40) and suspended solids (r = –0.27). Moreover, strong stratification in the Shihwa Lake has accelerated harmful algal blooms and hypoxia. The non-eutrophic states (SOM 1–3) are characterized by macro-tidal estuaries exhibiting a tolerance to pollution with nitrogen-containing nutrients and retarding any tendency toward stratification. SOM 1 (winter) is more distinct from SOM 4 due to higher suspended solids (>50 mg l−1) caused by resuspension that induces light limitation and low chlorophyll-a (<5 µg l−1). In addition, eutrophication-induced shifts in phytoplankton communities are noticed during all the seasons in Gyeonggi Bay. Overall, SOM showed high performance for visualization and abstraction of ecological data and could serve as an efficient ecological map that can specify blooming regions and provide a comprehensive view on the eutrophication process in a macrotidal estuary.

scholarly journals Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective

2020 ◽  
Author(s):  
Edgar Santos‐Fernandez ◽  
Erin E. Peterson ◽  
Julie Vercelloni ◽  
Em Rushworth ◽  
Kerrie Mengersen
Keyword(s):  
Ecological Data ◽  
Misclassification Errors
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