Opportunistically collected species observation data, or citizen science data, are increasingly available. Importantly, they’re also becoming available for regions of the world and species for which few other data are available, and they may be able to fill a data gap.
In Sweden, over 60 million citizen science observations have been collected – an impressive number given that Sweden has a population of about 10 million people and that the Swedish Species Observation System, Artportalen, was created in 2000. For bird-watchers (or plant, fungi, or other animal enthusiasts), this is a good website to bookmark. It will give you a bit of help in finding species and as a bonus, has a lot of pretty pictures of interesting species. Given the amount of data citizen science can provide in areas with few other data, it’s important to evaluate whether they can be used reliably to answer questions in applied ecology or conservation. Continue reading →
Opportunistiskt insamlade artobservationer av frivilliga, så kallade medborgarforskningsdata, blir alltmer tillgängliga. Dessa data har potentialen att fylla ett databehov för olika regioner i världen och arter för vilka få andra data är tillgängliga.
I Sverige har över 60 miljoner artobservationer samlats in av frivilliga i Artportalen – ett imponerande antal med tanke på att Sverige har en befolkning på cirka 10 miljoner människor och att webbplatsen endast har funnits sedan år 2000. För fågelskådare (eller växt-, svamp-, andra djurentusiaster), är Artportalen en bra hemsida att bokmärka om man vill ha lite hjälp med att hitta arter eller tycker om att titta på vackra bilder på arter. Globalt samlas ett stort antal sådana uppgifter för artförekomst i Global Biodiversity Information Facility. Med tanke på den mängd data som medborgarforskare kan tillhandahålla för områden med få andra data är det viktigt att utvärdera om de kan användas för att tillförlitligt besvara frågor inom grundläggande ekologi eller naturvård. Continue reading →
The ANDe system can help researchers tell whether endangered species are present.
In recent years, there have been a lot of studies on the use of environmental DNA (eDNA) for species detection and monitoring. This method takes advantage of the fact that organisms shed DNA into the environment in the form of urine, feces, or cells from tissue such as skin. As this DNA stays in the environment, we can use molecular techniques to search for traces of it. By doing this, we can determine if a species lives in a particular place.
The use of molecular methods for monitoring and surveillance of organisms in aquatic and marine systems has become more and more common. We’ve since expanded this technology this through using both captured whole organisms and collecting/filtering environmental DNA (eDNA). These methods naturally migrated from single species, active surveillance methods towards using high throughput sequencing as a method of passive surveillance via metabarcoding.
I’d recommend this paper to all researchers and management groups interested in applying metabarcoding techniques to answer both experimental and applied questions. The design of this article will provide both experienced researchers and those new to the field with important information to further this rapidly expanding field.
Current eDNA sampling technologies consist mainly of do‐it‐yourself solutions. The lack of purpose‐built sampling equipment is limiting the efficiency and standardization of eDNA studies. So, Thomas et al. (a team of molecular ecologists and engineers) designed ANDe™.
In this video, the authors highlight the key features and benefits of ANDe™. This integrated system includes a backpack-portable pump that integrates sensor feedback, a pole extension with remote pump controller, custom‐made filter housings in single‐use packets for each sampling site and on-board sample storage.
Researchers are increasingly interested in how social behaviour influences a range of biological processes. Social data have the interesting mathematical property that the number of potential connections among individuals is typically much larger than the number of individuals (because individuals can interact with every other member of their group). This introduces a huge challenge when it comes to collecting data on social interactions—not only does the amount of data needed increase exponentially with group size, the data can also be more difficult to record.
Larger groups have more simultaneous interactions, making it harder for observers to capture a complete or representative sample. It’s also more difficult for observers to tell individuals apart in larger groups. Coloured markers are often used to distinguish different members of a group – the bigger the group, the more complex the markers are needed.
Group-level properties or behaviours can also emerge or change rapidly over time or depending on the situation. This means that observations have to be made at high temporal resolution. To study social behaviour with group sizes that resemble those occurring in nature, we need new techniques to extract sufficient information from social groups. Continue reading →
Existe un creciente interés por parte de muchos investigadores por entender cómo el comportamiento social de los animales influencia otros procesos biológicos. Sin embargo, estudiar las interacciones entre múltiples individuos presenta un enorme reto metodológico, ya que el número de potenciales interacciones simultáneas aumenta, casi exponencialmente, con el tamaño del grupo (cada individuo puede interactuar con todos los demás miembros del grupo). Además, la cantidad de datos necesarios para un análisis robusto también se incrementa, haciendo difícil que los registros sean completos y representativos. Continue reading →
The Global Pollen Project is an online, freely available tool and data source developed to help people identify and disseminate palynological resources. Palynology – the study of pollen grains and other spores – is used across many fields of study including modern and fossil vegetation dynamics, forensic sciences, pollination, and beekeeping. To help make pollen identification quicker and more transparent, we developed the Global Pollen Project (GPP) – an open, peer-reviewed database of global pollen morphology, where content and expertise is crowdsourced from across the world. Our approach to developing this tool was open: open code, open data, open access. It connects to other data services, including the Global Biodiversity Information Facility and Neotoma Palaeoecology Database, to provide occurrence data for each taxon, alongside pollen images and metadata. Continue reading →
Some individuals survive and reproduce better than others. Traits that help them do so may be passed on to the next generation, leading to evolutionary change. Because of this, evolutionary biologists are interested in what differentiates the winners from the losers – how do their traits differ, and by how much? These differences are known as natural selection.
Linear and Nonlinear Selection
Traditionally, natural selection is separated into linear selection (differences in average trait values) and nonlinear selection (any other differences in trait distributions between winners and the rest). For example, successful individuals might be unusually close to average: this is known as stabilizing selection. Alternatively, winners might split into two camps, some with unusually high trait values, and others with unusually low trait values. This is disruptive selection (famously thought to explain the ur-origin of sperm and eggs). Stabilizing and disruptive selection are important types of nonlinear selection. In general, though, the trait distribution of successful individuals can differ from the general population in arbitrarily complicated ways.
When individuals with larger trait values have higher fitness on average (left panel), the trait distribution of successful individuals is shifted towards the right (right panel, orange curve). The difference in mean trait values between the winners and the general population is called linear selection.
The standard approach to quantifying natural selection, developed by Lande and Arnold, does not allow for comparable metrics between linear (i.e. selection on the mean phenotype) and nonlinear (i.e. selection on all other aspects of the phenotypic distribution, including variance and the number of modes) selection gradients. Jonathan Henshaw’s winning submission provides the first integrated measure of the strength of selection that applies across qualitatively different selection regimes (e.g. directional, stabilizing or disruptive selection). Continue reading →