What is he best known for?
The fields of study he is best known for:
- Ecology
- Social psychology
- Social science
His primary areas of study are Ecology, Social network, Guppy, Poecilia and Animal ecology.
Darren P. Croft combines subjects such as Zoology, Reciprocal altruism and Boldness with his study of Ecology.
Darren P. Croft has included themes like Developmental psychology and Interpersonal ties in his Boldness study.
His study in the fields of Social network analysis under the domain of Social network overlaps with other disciplines such as Social heuristics.
His studies in Guppy integrate themes in fields like Harassment and Poeciliidae.
Animal ecology is often connected to Behavioural sciences in his work.
His most cited work include:
- Exploring Animal Social Networks (503 citations)
- Social network theory in the behavioural sciences: potential applications (310 citations)
- Hypothesis testing in animal social networks (256 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Ecology, Social network, Guppy, Poecilia and Zoology.
His Ecology study integrates concerns from other disciplines, such as Evolutionary biology, Demography and Boldness.
Darren P. Croft interconnects Data science, Animal ecology, Behavioural sciences and Interpersonal ties in the investigation of issues within Social network.
His Guppy study which covers Social relation that intersects with Reciprocal altruism and Social preferences.
His Poecilia research is multidisciplinary, relying on both Context, Preference and Poeciliidae.
The study incorporates disciplines such as Social environment and Reproduction in addition to Zoology.
He most often published in these fields:
- Ecology (44.44%)
- Social network (26.80%)
- Guppy (20.92%)
What were the highlights of his more recent work (between 2018-2021)?
- Social network (26.80%)
- Evolutionary biology (11.11%)
- Demography (9.15%)
In recent papers he was focusing on the following fields of study:
Darren P. Croft spends much of his time researching Social network, Evolutionary biology, Demography, Guppy and Poecilia.
His Social network research includes themes of Statistical hypothesis testing, Regression analysis, Social preferences and Animal ecology.
His work in Evolutionary biology addresses subjects such as Mate choice, which are connected to disciplines such as Crypsis and Sexual selection.
His research investigates the connection between Demography and topics such as Sociality that intersect with problems in Flock.
His work deals with themes such as Boldness, Predation, Developmental psychology, Foraging and Social environment, which intersect with Guppy.
His Poecilia research includes elements of Zoology, Intraspecific competition, Context and Predator.
Between 2018 and 2021, his most popular works were:
- Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations. (26 citations)
- Measuring the complexity of social associations using mixture models (17 citations)
- Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models (9 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Social psychology
- Social science
His main research concerns Social network, Demography, Statistical hypothesis testing, Theoretical computer science and Regression analysis.
His Social network research is multidisciplinary, incorporating elements of Social relation, Social preferences, Network simulation, Statistical model and Set.
The Demography study combines topics in areas such as National park, Herd, Poaching and Reproduction.
His study in Statistical hypothesis testing is interdisciplinary in nature, drawing from both Sampling, Social network analysis, Null hypothesis and Regression.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Null model and Resampling.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
