Há alguns dias, me deparei com um interessante vídeo sobre os chamados “fósseis vivos”. O vídeo focou mais nos problemas de usá-los como argumentos contra a teoria da evolução, e aproveitei a oportunidade para falar mais sobre essas linhagens longevas.
‘Fóssil vivo‘ é um termo usado para descrever linhagens que acredita-se terem se originado há muito tempo e que mantêm características que se assemelham a seus parentes fósseis. Alguns exemplos bem conhecidos dessas linhagens são os Tuatara da Nova Zelândia (Sphenodon punctatus) e as árvores Gingkos (Gingko biloba).
A couple of days ago I came across a nice video (in Portuguese only, sorry) about so-called “living fossils”. The video focused on the problems of using them as arguments against evolution. But I’d like to take the opportunity to talk more about these long-lived lineages.
‘Living fossil’ is a term used to describe lineages that are thought to have been around for a very long time and retain characteristics that resemble of their fossil relatives. A couple of well-known examples of these lineages are the Tuatara of New Zealand (Sphenodon punctatus) and the Gingko tree (Gingko biloba).
Minimising the effects the ongoing Anthropocene mass extinction has become one of the main challenges of our era. The data suggest that the current rate of species loss is 100–1,000 greater than the background rates seen in the geological record. “But does it really matter if species are lost?” This question has permeated social and political debates. It’s usually used to demean conservation efforts. But it has also intrigued conservation scientists.
We know that species don’t occur alone in their environment. They’re entangled by their interactions, forming complex networks. In these networks the loss of one species may result in the loss of other species that depend on it. This process is known as co-extinction. Estimates of the magnitude of past and future extinction rates have often failed to account for the interdependence among species and the consequences of primary species loss on other species though. Continue reading →
I had the pleasure of delivering one of the plenary talks at the first (hopefully of many) Crossing the Palaeontological – Ecological Gap meeting held in the University of Leeds on August 30th and 31st. I’m a geologist and a botanist, so this is a topic that’s close to my heart and my professional interests.
As we move into an ecologically uncertain future with pressures of climate change, land-use change and resource limitations, the fossil record offers the only truly long-term record of how Earth’s ecosystems respond to major environmental upheaval driven by climate change events. The fossil record is, of course, not without its problems – there are gaps, not everything fossilises in the same way or numbers, and comparisons to today’s ecology are extremely difficult. It’s these difficulties (and other challenges) that make the uniting of palaeontology and ecology essential to fully address how plants, animals and other organisms have responded to major changes in the past. Perhaps uniting them could give us an idea of what to expect in our near-term future, as carbon dioxide levels return to those not previously experienced on Earth since the Pliocene, over 2 million years ago. Continue reading →
Today is the first day of the Crossing the Palaeontological-Ecological Gap (CPEG) conference. The aim of the conference is to open a dialogue between palaeontologists and ecologists who work on similar questions but across vastly different timescales. This splitting of temporal scales tends to make communication, data integration and synthesis in ecology harder. A lot of this comes from the fact that palaeontologists and ecologists tend to publish in different journals and attend different meetings.
Imagine you’re the manager of a national park. One that’s rich in endemic biodiversity found nowhere else on the planet. It’s under the influence of multiple human pressures causing irreversible declines in the biodiversity, possibly even leading to the extinction of some of the species. You’re working with a complex system of multiple species and threats, limited knowledge of which threats are causing the biggest declines and limited resources. How do you decide what course of action to take to conserve the biodiversity of the park? This is the dilemma faced by biodiversity managers across the globe.
Climate change could cause the extinction of one in six species and change the abundance and distribution of those that remain (Urban, 2015). This doesn’t necessarily mean that one in six species in your backyard will go extinct though. Climate change impacts will vary greatly around the globe, with some regions seeing disproportionate effects.
The degree to which climate change will affect species in your region depends on many factors (e.g., land use and species traits), but the amount of climate change that species experience in your region – known as climate change exposure – will certainly be important. For that reason, measuring and mapping climate change exposure is critical for predicting where climate change will have the biggest impacts. Yet, biologists have no agreed upon method to measure exposure and different methods can produce dramatically different results.
A Simple Measure of Exposure and its Limitations
Climate can be defined as a statistical description of weather (e.g., temperature, precipitation) over the course of a long time period, usually 30 years. Most often climate is reduced to the average value of a particular weather variable over a 30-year period of interest. Climate change is then measured as the difference between the averages in two time periods; say the predicted average between 2070-2099 minus the average between 1971-2000.
Projected changes in annual average temperature between 1971-2000 and 2070-2099.
For example, the map to the left shows projected exposure to changes in average annual temperature. This map suggests that species in the arctic will be exposed to the most temperature change while species in the southern hemisphere will experience the least change. However, there are many problems with this interpretation. Continue reading →
To truly understand how species’ distributions vary through space and time, biogeographers often have to make use of analytical techniques from a wide array of disciplines. As such, these papers cover advances in fields such as evolutionary analysis, biodiversity definitions, species distribution modelling, remote sensing and more. They also reflect the growing understanding that biogeography can include experiments and highlight the increasing number of software packages focused towards biogeography.
This Virtual Issue was compiled by Methods in Ecology and Evolution Associate Editors Pedro Peres-Neto and Will Pearse (both of whom are involved in the conference). All of the articles in this Virtual Issue are free for a limited time and we have a little bit more information about each of the papers included here: Continue reading →
Years of research on the evolution of ancient life, including the dinosaurs, have been questioned after a fatal flaw in the way fossil data are analysed was exposed by scientists from the universities of Reading and Bristol.
Studies based on the apparently flawed method have suggested Earth’s biodiversity remained relatively stable – close to maximum carrying capacity – and hinted many signs of species becoming rapidly extinct are merely reflections on the poor quality of the fossil record at that time.
However, new research by scientists at the University of Reading suggests the history of the planet’s biodiversity may have been more dynamic than recently suggested, with bursts of new species appearing, along with crashes and more stable periods.
This month’s issue contains two Applications articles and two Open Access articles, all of which are freely available.
– Plant-O-Matic: A free iOS application that combines the species distribution models with the location services built into a mobile device to provide users with a list of all plant species expected to occur in the 100 × 100 km geographic grid cell corresponding to the user’s location.
– RClone: An R package built upon genclone software which includes functions to handle clonal data sets, allowing:
Checking for data set reliability to discriminate multilocus genotypes (MLGs)
Ascertainment of MLG and semi-automatic determination of clonal lineages (MLL)
Genotypic richness and evenness indices calculation based on MLGs or MLLs
Describing several spatial components of clonality