Tundra shrub expansion in a warming climate and the influence of data type on models of habitat suitability

Figures & data

Figure 1. Map of the study area in the Beaufort Delta region. (a) The extent of main map (red) in North America and (b) an enlargement of Herschel Island (the green rectangle on the main map). (c) A photograph of typical terrain and ground cover on Banks Island (photo by T. Lantz). (d) A photograph of typical terrain and ground cover on the Tuktoyaktuk Coastlands (photo by J. Seider).

Table 1. Percent of all plots sampled with observed presence (prevalence) of modeled species from field survey data.

Species	Prevalence (%)
Alder	18.2
Birch	44.1
Labrador tea	34.9
Bog bilberry	25.6
Lingonberry	43.7

Table 2. Bioclimatic variables from WorldClim (Fick and Hijmans 2017) used in this analysis.

Identifier	Bioclimatic variable	Variable description
BIO_01	Annual mean temperature	Average of monthly mean temperature (°C)
BIO_02	Mean diurnal range	Average of monthly temperature ranges (°C)
BIO_03	Isothermality	Mean diurnal temperature range divided by annual temperature range
BIO_04	Temperature seasonality	Temperature variability over the year measured as a ratio of the standard deviation of the monthly mean temperatures to the mean monthly temperature (%)
BIO_11	Mean temperature of the coldest quarter	Mean temperature of the coldest consecutive three months (°C)
BIO_15	Precipitation seasonality	Variation in monthly precipitation over the year measured as a ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation (%)
BIO_18	Precipitation of the warmest quarter	Sum of monthly precipitation values for the three warmest consecutive months

Note. Variable descriptions are from O’Donnell and Ignizio (2012).

Table 3. Model descriptions.

Algorithm	R package	Description
Generalized linear model	glm	Extension of ordinary linear regression allowing for error distributions other than Gaussian
Generalized boosted model	gbm	Nonparametric method using a combination of decision tree models implemented with boosting methods for weighted randomization of data in each successive tree
Multiple adaptive regression splines	earth	Nonparametric method creating multiple linear regression models (with different slopes) across the range of data and finding the optimal connection points between each segment to create one model
Artificial neural networks	nnet	Single hidden layer neural network using backpropagation to adjust weights and biases to increase model performance.
Random forest	randomForest	Ensemble decision tree algorithm incorporating randomized bagging method to a subset of available data used in each individual tree. Data are run through all trees and the final classification is based on the majority vote.

Table 4. Description of performance metrics used for model evaluation.

Performance metric	Description	Range
Percentage correctly classified	Percentage of independent evaluation data correctly classified as either presence or absence	[0, 1]
Sensitivity	Probability of a true positive classification on evaluation data	[0, 1]
Specificity	Probability of a true negative classification on evaluation data	[0, 1]
Area under the receiver operating characteristic curve	Threshold-independent measure calculating the area under the receiver operating characteristic curve (plot of false positives as a function of sensitivity for all possible threshold values)	[0, 1]
True skill statistic	Modified form of Cohen’s kappa to theoretically remove dependence on species prevalence	[−1, 1]
Overprediction rate	Proportion of false presences among all predictions	[0, 1]
Bias	Average difference between actual observation and predicted probability of observed presence in independent data	—

Table 5. Mean and standard deviation of habitat suitability across the entire study area for each model under current (1970 to 2000) and future (2061 to 2080) climate.

		True absence		Pseudo-absence
		Mean	Standard deviation	Mean	Standard deviation
Alder (Alnus viridis)	Current	0.109	0.199	0.166	0.229
	Future	0.329	0.084	0.375	0.129
	Change	0.220	—	0.210	—
Birch (Betula nana and B. glandulosa)	Current	0.298	0.292	0.236	0.287
	Future	0.446	0.141	0.335	0.096
	Change	0.149	—	0.099	—
Labrador tea (Ledum decumbens)	Current	0.279	0.267	0.217	0.282
	Future	0.359	0.125	0.317	0.053
	Change	0.080	—	0.100	—
Bog bilberry (Vaccinium uliginosum)	Current	0.274	0.251	0.245	0.307
	Future	0.505	0.063	0.274	0.104
	Change	0.230	—	0.029	—
Lingonberry (V. vitis-idaea)	Current	0.287	0.286	0.227	0.303
	Future	0.655	0.103	0.308	0.072
	Change	0.369	—	0.081	—

Figure 2. Ensemble habitat suitability maps (gray box) for alder (Alnus viridis) projected under current and future climate conditions using true absence and pseudo-absence models. Banks Island is inset over the mainland portion of the study area for enhanced visualization. Plots (A)–(D) correspond to differences between climate projections and data types along the columns and rows.

Figure 3. Ensemble habitat suitability maps (gray box) for birch (Betula nana and B. glandulosa) projected under current and future climate conditions using true absence and pseudo-absence models. Banks Island is inset over the mainland portion of the study area for enhanced visualization. Plots (A)–(D) correspond to differences between climate projections and data types along the columns and rows.

Figure 4. Ensemble habitat suitability maps (gray box) for marsh Labrador tea (Ledum decumbens) projected under current and future climate conditions using true absence and pseudo-absence models. Banks Island is inset over the mainland portion of the study area for enhanced visualization. Plots (A)–(D) correspond to differences between climate projections and data types along the columns and rows.

Figure 5. Ensemble habitat suitability maps (gray box) for bog bilberry (Vaccinium uliginosum) projected under current and future climate conditions using true absence and pseudo-absence models. Banks Island is inset over the mainland portion of the study area for enhanced visualization. Plots (A)–(D) correspond to differences between climate projections and data types along the columns and rows.

Figure 6. Ensemble habitat suitability maps (gray box) for lingonberry (Vaccinium vitis-idaea) projected under current and future climate conditions using true absences and pseudo-absences models. Banks Island is inset over the mainland portion of the study area for enhanced visualization. Plots (A)–(D) correspond to differences between climate projections and data types along the columns and rows.

Table 6. Ensemble model performance metrics calculated using independent data.

		Threshold	PCC	Sensitivity	Specificity	AUC	TSS	OPR	Bias
A. viridis	TA	0.272	0.857	0.800	0.909	0.882	0.709	0.111	0.204
	PA	0.513	0.857	0.900	0.818	0.836	0.718	0.182	−0.037
Betula spp.	TA	0.541	0.789	0.900	0.667	0.744	0.567	0.250	−0.014
	PA	0.570	0.810	0.909	0.700	0.964	0.609	0.231	−0.046
L. decumbens	TA	0.508	0.750	0.808	0.667	0.879	0.485	0.250	0.042
	PA	0.527	0.905	1.000	0.800	0.936	0.800	0.154	−0.003
V. uliginosum	TA	0.433	0.800	0.818	0.778	0.798	0.596	0.182	0.117
	PA	0.558	0.667	0.818	0.500	0.773	0.318	0.357	−0.034
V. vitis-idaea	TA	0.390	0.789	0.875	0.727	0.852	0.602	0.300	0.031
	PA	0.554	0.714	0.818	0.600	0.745	0.418	0.308	−0.030
Average	TA	0.429	0.797	0.842	0.750	0.831	0.592	0.219	0.076
	PA	0.544	0.791	0.889	0.684	0.851	0.573	0.246	−0.030
Null model		0.469	0.478	0.385	0.600	0.535	−0.015	0.444	0.096

Note. Bolded values indicate the highest performing model for each species (higher value for all except OPR and bias) between true absence (TA) and pseudo-absence models (PA). Null model created using randomized presence and absence locations. PCC = percentage correctly classified; AUC = area under the receiver operating characteristic curve; TSS = true skill statistic; OPR = overprediction rate.

Table 7. Ranking of the top five variables for each model in order of importance (1 is most important) for each species for both true absence (TA) and pseudo-absence (PA) models.

Species		Annual mean temperature	Mean diurnal range	Isothermality	Temperature seasonality	Mean temperature of the coldest quarter	Precipitation seasonality	Precipitation of the warmest quarter	Elevation	Slope	Ruggedness
Alder (Alnus viridis)	TA	5	—	4	—	—	1	2	3	—	—
	PA	5	—	3	—	—	4	2	1	—	—
Birch (Betula nana and B. glandulosa)	TA	1	—	—	—	5	2	3	4	—	—
	PA	3	2	4	—	1	—	—	5	—	—
Labrador tea (Ledum decumbens)	TA	1	—	3	—	—	2	5	4	—	—
	PA	1	—	—	—	5	4	2	3	—	—
Bog bilberry (Vaccinium uliginosum)	TA	3	1	—	—	—	—	5	2	4	—
	PA	3	—	—	5	2	4	1	—	—	—
Lingonberry (V. vitis-idaea)	TA	1	—	5	—	4	2	3	—	—	—
	PA	4	—	5	—	2	—	1	3	—	—

Note. The top three variables in each model are shown in bold red and the five least important variables are noted with a dash. TA = true absence; PA = pseudo-absence.

Figure 7. Importance scores for the predictor variables used in true absence ensemble SDM for each species.

Fick, S. E., and R. J. Hijmans. 2017. WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12):4302–15. doi:10.1002/joc.5086.

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