Bibliographic Information: Allen, J.M., Leininger, T.J., Hurd, J.D. et al. Landscape Ecol (2013) Socio-economics drive woody invasive plant richness in New England, USA through forest fragmentation.  28: 1671. https://doi.org/10.1007/s10980-013-9916-7.

The research objectives of this paper are:

1. To determine the strength and direction relationship between forest fragmentation and woody invasive plant richness.

2. To use selected socioeconomic and environmental factors to explain the land use and land  cover (LULC) in addition to the observed patterns of forest fragmentation .

Building on previous observations about invasive species being more prevalent around the forest edge (in comparison to the forest core), the authors attempted to assess this theory in New England in the United States. Additionally, because urban centres are rapidly developing in this area, they wanted to determine the extent to which certain socio-economic (e.g. single family housing) and environmental (e.g. elevation) factors can be used to predict forest fragmentation change.

This research used a wide range of data and technological processes :

1. three categories of data including landscape response data and species richness data (collected from NOAA and citizen science initiatives), and socioeconomic data (from the US Census)
2. Methods of satellite data geoprocessing and LUCL classification (GIS), spatial analysis and statistical models

Hierarchical bayesian modelling was used to predict changes in forest fragmentation and land use class distribution by each socio-economic and environmental variable given the properties of the rest of the data, with the outputs (such as spatial random effects) displayed on ArcGIS. It was evident from the model that socio-economic factors (driven by population) do explain forest fragmentation patterns in the region. Additionally, invasive species richness data was mapped and statistically analysed to reveal that, as expected, woody invasive richness is significantly and positively associated with forest edge areas and significantly and negatively associated with forest core areas – thus, future fragmentation is likely to increase the probability of woody invasive species spread.

What worked well? The methodology was robust and highly replicable – the method can be used the model forest fragmentation patterns in other regions. The citizen science element allowed for easy data-collection and likely engaged many non-expert volunteers in local ecology. However, due to the scarcity of volunteers in northern New England, this method meant richness data was not evenly distributed.

For these reasons, we rated this paper 8.5/10.

Reference: Weisberg, P. J., Ko, D., Py, C., & Bauer, J. M. (2008). Modeling fire and landform influences on the distribution of old-growth pinyon-juniper woodland. Landscape Ecology, 23(8), 931-943.

I chose to review this presentation by Bowen Lan and Leian Cindy Chen because I am interested to learn more about the relationship between fire and woodland, particularly in the Western US where we are seeing a rising frequency of forest fires, and how this trend is influences the distribution of species across the landscape.

Old-growth Pinyon and Utah juniper forests are commonly distributed throughout the Western US. A key knowledge gap is the reason for the lack of diversity in the area –  is this driven by fire history? The authors created models of old growth distribution, taking into account topography and fuel in order to address the following research objectives:

1. Determine which fire risk component is most strongly associated with old growth distribution.
2.  Assess the strength of the association between fire and spatial distribution in pinyon-juniper woodland.

The study site was a watershed area in Nevada. The authors mapped the current distribution of old-growth woodland and made models for predicted old growth distribution using cellular automated simulation to predict old growth distribution by adding fire and topographic variables to their models.

Model 1:

In ArcGIS, the authors modelled both the spatial variability in fuel loading in addition to the topographic convergence and solar radiation indices. These indices were z-standardisted into the three categories: low, moderate, and high. Patterns were then identified, including the observation that upper slope sites have a low probability of fire development due to the low fuel availability and greater exposure.

Model 2: 

The authors also modelled the topographic barriers to the spread of fire, using GIS to identify and mark the locations of ridge lines, proximity to rock outcrops, and rating on the wind exposure index (which combines meteorological and topographic data).

Model 3:

The final model was a combination of fuel loading factors and topographic barriers (thus, a mix of biotic and abiotic variables).

Ultimately, it was determined that the spatial distribution of fuel material influenced the agre structure of the landscape to a greater extent than topographic barriers.

Bowen and Cindy gave the study a 7/10 because the models were novel and robust and could be applied to future spatial distribution studies, however landscape dynamics are complex so they believe the authors could have incorporated more independent variables into their models.