Tag Archives: Species Abundance

Reconnecting the Web of Life: Rewiring and Network Robustness

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Post provided by VINICIUS A. G. BASTAZINI, JEF VIZENTIN-BUGONI and JINELLE H. SPERRY

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Species Loss and Cascading Effects

Scale-throated Hermit (Phaethornis eurynome). ©Pedro Lorenzo.

Scale-throated Hermit (Phaethornis eurynome). ©Pedro Lorenzo.

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 →

Birds and Climate in Space and Time: Separating Spatial and Temporal Effects of Climate Change on Wildlife

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Post provided by Cornelia Oedekoven

The Standard Method

When trying to understand how wildlife, for example a bird species, may react to climate change scientists generally study how species numbers vary in relation to climatic or weather variables (e.g. Renwick et al. 2012, Johnston et al. 2013). The way this tends to be done is by gathering information (data!) about bird numbers as well as the weather variables (for example temperature) in several locations (i.e. in space) and fitting a regression model to these data to detect and illustrate how bird numbers go up or down with temperature.

Data on bird numbers and temperatures in several locations lets researchers see the relationship between the two.

Data on bird numbers and temperatures in several locations lets researchers see the relationship between the two.

This relationship is then used to forecast how bird numbers may change along with potential temperature changes in the future (i.e. in time), for example due to climate change.

Relationships between bird numbers and temperature in a given location are often used to forecast changes in bird numbers with expected changes in temperatures over time.

Relationships between bird numbers and temperature in a given location are often used to forecast changes in bird numbers with expected changes in temperatures over time.

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‘Size’ and ‘Shape’ in the Measurement of Multivariate Proximity

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Ordination and clustering methods are widely applied to ecological data that are non-negative (like species abundances or biomasses). These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as:

  1. Ordinations of the units, where interpoint distances optimally display the measured differences
  2. Clustering the units into homogeneous clusters
  3. Assessing differences between pre-specified groups of units (e.g. regions, periods, treatment–control groups)

In this video, Michael Greenacre introduces his new article, ‘‘Size’ and ‘Shape’ in the Measurement of Multivariate Proximity’, published in Methods in Ecology and Evolution, May 2017. In the context of species abundances, for example, he explains how much a chosen proximity measure captures the difference in “size” between two samples, i.e. difference in overall abundances, and differences in “shape”, i.e. differences in compositions or relative abundances.  He shows that the popular Bray-Curtis dissimilarity inevitably includes a part of the “size” difference in its measurement of multivariate proximity.

This video is based on the article ‘‘Size’ and ‘shape’ in the measurement of multivariate proximity‘ by Michael Greenacre.