Simple yet effective: historical proximity variables improve the species distribution models for invasive giant hogweed (Heracleum mantegazzianum s.l.) in Poland

Species distribution models are scarcely applicable to invasive species because of their breaking of the models’ assumptions. So far, few mechanistic, semi-mechanistic or statistical solutions like dispersal constraints or propagule limitation have been applied. We evaluated a novel quasi-semi-mechanistic approach for regional scale models, using historical proximity variables (HPV) representing a state of the population in a given moment in the past. Our aim was to test the effects of addition of HPV sets of different minimal recentness, information capacity and the total number of variables on the quality of the species distribution model for Heracleum mantegazzianum on 116000 km2 in Poland. As environmental predictors, we used fragments of 103 1×1 km, world- wide, free-access rasters from WorldGrids.org. Single and ensemble models were computed using BIOMOD2 package 3.1.47 working in R environment 3.1.0. The addition of HPV improved the quality of single and ensemble models from poor to good and excellent. The quality was the highest for the variants with HPVs based on the distance from the most recent past occurrences. It was mostly affected by the algorithm type, but all HPV traits (minimal recentness, information capacity, model type or the number of the time periods) were significantly important determinants. The addition of HPVs improved the quality of current projections, raising the occurrence probability in regions where the species had occurred before. We conclude that HPV addition enables semi-realistic estimation of the rate of spread and can be applied to the short-term forecasting of invasive or declining species, which also break equal-dispersal probability assumptions

Do David and Goliath play the same game? Explanation of the abundance of rare and frequent invasive alien plants in urban woodlands in Warsaw, Poland

Data used to model the abundance of rare and frequent invasive alien plant in urban forests in Warsa

Replication Data for: Simple yet effective: historical proximity variables improve the species distribution models for invasive giant hogweed (Heracleum mantegazzianum s.l.) in Poland

Species distribution models are scarcely applicable to invasive species because of their breaking of the models’ assumptions. So far, few mechanistic, semi-mechanistic or statistical solutions like dispersal constraints or propagule limitation have been applied. We evaluated a novel quasi-semi-mechanistic approach for regional scale models, using historical proximity variables (HPV) representing a state of the population in a given moment in the past. Our aim was to test the effects of addition of HPV sets of different minimal recentness, information capacity and the total number of variables on the quality of the species distribution model for Heracleum mantegazzianum on 116000 km2 in Poland. As environmental predictors, we used fragments of 103 1×1 km, world- wide, free-access rasters from WorldGrids.org. Single and ensemble models were computed using BIOMOD2 package 3.1.47 working in R environment 3.1.0. The addition of HPV improved the quality of single and ensemble models from poor to good and excellent. The quality was the highest for the variants with HPVs based on the distance form the most recent past occurrences. It was mostly affected by the algorithm type, but all HPV traits (minimal recentness, information capacity, model type or the number of the time periods) were significantly important determinants. The addition of HPVs improved the quality of current projections, raising the occurrence probability in regions where the species had occurred before. We conclude that HPV addition enables semi-realistic estimation of the rate of spread and can be applied to the short-term forecasting of invasive or declining species, which also break equal-dispersal probability assumptions

Sum of importance of groups of predictors for frequent and rare species.

<p>Sum of importance of groups of predictors for frequent and rare species.</p

Do David and Goliath Play the Same Game? Explanation of the Abundance of Rare and Frequent Invasive Alien Plants in Urban Woodlands in Warsaw, Poland

<div><p>Invasive Alien Plants occur in numbers differing by orders of magnitude at subsequent invasion stages. Effective sampling and quantifying niches of rare invasive plants are quite problematic. The aim of this paper is an estimation of the influence of invasive plants frequency on the explanation of their local abundance. We attempted to achieve it through: (1) assessment of occurrence of self-regenerating invasive plants in urban woodlands, (2) comparison of Random Forest modelling results for frequent and rare species. We hypothesized that the abundance of frequent species would be explained better than that of rare ones and that both rare and frequent species share a common hierarchy of the most important determinants. We found 15 taxa in almost two thirds of 1040 plots with a total number of 1068 occurrences. There were recorded 6 taxa of high frequency–<i>Prunus serotina</i>, <i>Quercus rubra</i>, <i>Acer negundo</i>, <i>Robinia pseudoacacia</i>, <i>Impatiens parviflora</i> and <i>Solidago</i> spp.–and 9 taxa of low frequency: <i>Acer saccharinum</i>, <i>Amelanchier spicata</i>, <i>Cornus</i> spp., <i>Fraxinus</i> spp., <i>Parthenocissus</i> spp., <i>Syringa vulgaris</i>, <i>Echinocystis lobata</i>, <i>Helianthus tuberosus</i>, <i>Reynoutria</i> spp. Random Forest’s models’ quality grows with the number of occurrences of frequent taxa but not of the rare ones. Both frequent and rare taxa share a similar hierarchy of predictors’ importance: Land use > Tree stand > Seed source and, for frequent taxa, Forest properties as well. We conclude that there is an ‘explanation jump’ at higher species frequencies, but rare species are surprisingly similar to frequent ones in their determinant’s hierarchy, with differences conforming with their respective stages of invasion.</p></div

The average importance of single predictors for frequent and rare species groups.

<p>The average importance of single predictors for frequent and rare species groups.</p

The sum of RF importance for variable groups for rare species.

<p>The numbers in circles are the subtotal of RF IncNodePurity importance based on the sum of squared residuals. The area of circles is proportional to the share of a given group in the sum of importance for the model of each species separately. Brown colour indicates woody and green indicates herbaceous species; N.A. = data not available.</p

Model quality and the frequency of IAP species.

<p>Model quality and the frequency of IAP species.</p

The sum of RF importance for variable groups for frequent species.

<p>The numbers in circles are the subtotal of RF IncNodePurity importance based on the sum of squared residuals. The area of circles is proportional to the share of a given group in the sum of importance for the model of each species separately. Brown colour indicates woody and green indicates herbaceous species; N.A. = data not available.</p

The occurrence and abundance of frequent and rare IAP taxa in municipal urban woodlands.

<p>Calculated on the dataset of 1040 Sampling Plots (SP).</p