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

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

Direct Regioselective Synthesis of Tetrazolium Salts by Activation of Secondary Amides under Mild Conditions

Tetrazolium salts are biologically active molecules that have found broad applications in biochemical assays. A regioselective synthesis of tetrazolium salts is described through a formal (3 + 2) cycloaddition. The possibility of employing simple amides and azides as starting material and the mild conditions allow a broad functional group tolerance

Relative importance (mean Z-score of the raw regression-type RF importance) and significance ≤ 0.01 of historical proximity variables (HPV) set attributes according to the Boruta meta-model of the BIOMOD2 model true skill statistics (TSS) values.

<p>Relative importance (mean Z-score of the raw regression-type RF importance) and significance ≤ 0.01 of historical proximity variables (HPV) set attributes according to the Boruta meta-model of the BIOMOD2 model true skill statistics (TSS) values.</p

Possible applications of historical proximity variables (HPV) in quasi semi-mechanistic species distribution models (SDMs).

<p>(a) The inclusion and estimation of the explicit spatio-temporal relationships between the current and the earlier distribution of the modelled species enables one to make a forecast using the distance from the current distribution as one of the predictors. (b) The substitution of the distance to the nearest earlier site with low or zero values makes possible computing the equivalent of the potential range for invasive species. (c) The same idea should be applicable to the modelling of the declining species, where the distance and the number of the previous sites should ameliorate the estimation of the current distribution.</p

The comparison of the historical proximity-based quasi semi-mechanistic approach to modelling of the invasive plant species with SAC in their distribution with existing statistical, mechanistic and semi-mechanistic approaches.

<p>The comparison of the historical proximity-based quasi semi-mechanistic approach to modelling of the invasive plant species with SAC in their distribution with existing statistical, mechanistic and semi-mechanistic approaches.</p

Projections of current, forecasted and potential distribution maps and their derivative products, produced using historical proximity variables (HPV) for the giant hogweed data from 2012, with distance to the nearest site 2 years earlier as an HPV variable [45].

<p>Maps a-c are weighted mean projections of ensemble models with 3 algorithms (GBM, RF, CTA), 3 sets of 1500PA, 3 repetitions. ROC = 0.971. BIOMOD2 3.1.25, R 3.0.2.</p

Generation process of historical proximity variables.

<p>Generation process of historical proximity variables.</p

TSS quality values of single (blue colour) and ensemble (green colour) BIOMOD2 models for variants with and without historical proximity variables (HPV).

<p>TSS quality values of single (blue colour) and ensemble (green colour) BIOMOD2 models for variants with and without historical proximity variables (HPV).</p

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

<div><p>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 <i>Heracleum mantegazzianum</i> on 116000 km² in Poland. As environmental predictors, we used fragments of 103 1×1 km, world- wide, free-access rasters from <a href="http://WorldGrids.org" target="_blank">WorldGrids.org</a>. 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.</p></div