Tag: John Vinson

As with Mod (2015), the integration of biotic interactions/variables into species distribution models is of interest.  It is already know that at small, local scales biotic interactions influence the distribution of species.  However, in order to integrate these interactions at a larger scale and in the future, a better understanding of how these interactions have influenced the species historically and dispersal.  The authors provide a review using studies involving species ranges, functional groups, and patterns of species richness.

The authors find that the biotic interactions have shaped the distribution of species beyond the local extent (10 km^2). The authors then suggest and review some ways to integrate biotic interactions within species distribution models.  These include using pairwise dependencies, using integrative predictors, and lastly hybridizing the models with dynamic models.

However, there are some problems with integrating these.  One possible problem is that species interactions may not be constant in time and space.  All the integrated models would assume that the interaction would be static between species. Any changes in species composition may affect these interactions through time and also space.  The authors close by calling for better data collection across scales and along environmental gradients.

Wisz, M. S. et al. 2013 The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol. Rev. 88, 15–30.

Due to the recent archiving and georeferencing of plant specimen collected over the past century, the distribution modeling of these species is now easily possible. The authors modeled the distribution of 6,828 species and determined the biological richness of areas in China.  In addition, they also investigated the drivers of the richness in the areas.

To prediction the distribution of the species, they used MaxEnt.  To analyze regions of species richness, they used ordination and Getis-Ord Gi* statistics finding hotspots of species richness. These also allowed them to determine the drivers of species richness and hotspots.

 

Of the predictors used, annual precipitation and temperature stability provided a major role in the observed species diversity.  However, when looking at the different regions, there were different drivers:

• SE – annual precipitation
• SW – topographic & temperature stability
• NW – water deficit
• NE – temperature instability

 

Fifteen uncorrelated variables plotted as predictors of environmental turnover calculated for 6560 woody species. Vectors are displayed only for the highly significant variables (P < 0.001) inferred from non-metric multidimensional scaling (NMDS) ordination. TAR: Temperature Annual Rang.

Zhang, M.-G., Slik, J. W. F. & Ma, K.-P. 2016 Using species distribution modeling to delineate the botanical richness patterns and phytogeographical regions of China. Sci. Rep. 6.

In relation to Mosomtai et al. (2016) (see Association of ecological factors with Rift Valley fever occurrence and mapping of risk zones in Kenya) these authors predict the distribution of the two vectors for Rift Valley fever (RVF).  Previous studies have looked at disease risk by finding areas with high vectors pressure or virus activity.  Here the authors investigate the impact of climate and environment on the presence of Aedes vegans and Culex poicilipes  (the two primary vectors of RFV).

Mosquito data was gathered from the Barkedji village (Ferlo area) during 2005 to 2006 across 79 sites.  After collecting the data, the Getis-Ord statistic was calculated to determine hotspots (adult mosquito abundance clusters).  The Getis-Ord Gi* measures the spatial clustering by identifying hotspots with a higher magnitude than expected from random chance. To deal with spatial autocorrelation, generalized linear mixed effect models were used.  The response/dependent variable was the calculated Getis-Ord Gi* of the hotspots.  The predictor/independent variables were rainfall, relative humidity, max and min temperature, NDVI, and distance from nearest pond.

For the Culex species, drops in the minimum temperature allows for an increase the occurrence of hotspots.  For the Aedes species, there is a negative relationship with relative humidity, max and min temperatures and hotspot occurrence. For both species, the distance to the nearest pond increases the occurrence of a hotspot. The authors close the paper by commenting that these models and understanding what promotes the occurrence of hotspots can lead to better vector control in the area.

Talla, C., Diallo, D., Dia, I., Ba, Y., Ndione, J.-A., Morse, A. P., Diop, A. & Diallo, M. 2016 Modelling hotspots of the two dominant Rift Valley fever vectors (Aedes vexans and Culex poicilipes) in Barkédji, Sénégal. Parasit. Vectors 9, 1.

The focus of the paper is to create a spatial risk map for Rift Valley Fever (RVF) using ecological and environmental variables.  RVF is a mosquito borne infection in vertebrates.  There are typically outbreak of the disease after periods of rainfall and high temperatures.  These outbreaks occur in 5 to 15 year intervals with flareups occurring between.  Previous studies that have looked at RVF occurrences have not used explicit ecological factors in their models.  Here the authors use explicit factors to map the risk of RVF in Kenya.  Areas of risk were defined as areas that were able to support the vectors in habitat suitability and population dynamics.

The authors use a generalized linear model to relate the ecological predictors to the occurrence data.  For the predictor variables, animal/livestock density, elevation, season length, small vegetation integral, soil ratio, and two principle components of evapotranspiration (PC1_ET and PC2_ET) were used.

Of the predictors, livestock density, small vegetation integral, and PC2_ET were the most significant variables.  In Kenya, the Tana River, Garissa, Isiolo and Lamu were the areas of highest risk. The authors also close by suggesting the integration of livestock movement and density along with the vegetation measurements into early warning systems.

Mosomtai, G., Evander, M., Sandström, P., Ahlm, C., Sang, R., Hassan, O. A., Affognon, H. & Landmann, T. 2016 Association of ecological factors with Rift Valley fever occurrence and mapping of risk zones in Kenya. Int. J. Infect. Dis. 46, 49–55.

 

Lyme disease, a a tick-borne disease caused by Borrelia burgdorferi,  has had an increasing number of cases occur in Mexico.  While the disease is rare in the Southern United States, it has been found to occur in Northern and Mid-Western US and Europe.  The authors investigate the distribution of potential tick vectors (ten Ixodoes spp. and Amblyomma cajennense [inclusion of this species as a vector is through personal communication]) in Mexico.

Occurrence data of the ticks was collected from prior publications and field surveys.  However, the occurrence data for ticks in Texas and Mexico was too sparse to do the models independently.  While the data used was collected in both Mexico and Texas, the authors only present the results for Mexico.  Environmental data was collected from WorldClim.  The distribution model used was MaxEnt and the trained for the Amblyomma cajennense  and the ten Ixodoes as a group.

From the results, there is spatial non concordance between the species.  Amblyomma cajennense  was mainly predicted to occur in mangrove and marsh regions at lower altitudes along the coast (red region).  The Ixodoes group are mainly found in oak and pine-oak forests.  These results highlight that if a species in the Ixodoes group is capable of transmitting the pathogen then the areas of highest risk are at high-altitude low temperature areas (which helps explain why Lyme disease is so rare in the southern US, high temperatures). However, if it is true that Amblyomma cajennense is capable of maintaining the pathogen in reservoir hosts, then the region extends  eastern lowlands of Mexico.

Illoldi-Rangel, P. et al. 2012 Species distribution models and ecological suitability analysis for potential tick vectors of Lyme disease in Mexico. J. Trop. Med. 2012.