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SUPPORTING INFORMATION FOR:
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FORECASTING RANGE EXPANSION INTO ECOLOGICAL TRAPS: CLIMATE-
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MEDIATED SHIFTS IN SEA TURTLE NESTING GROUNDS AND HUMAN
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DEVELOPMENT
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David A. Pike*
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School of Marine and Tropical Biology and Centre for Tropical Environmental Sustainability
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Science, James Cook University, Townsville, Queensland 4811 Australia
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*
Correspondence: telephone: +612 9351 4660; fax: +612 4725 1570; email:
david.pike22@gmail.com
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SUPPORTING INFORMATION
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Figure S1: Comparison of the three best-supported MaxEnt models. Models used the same set of
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five variables describing the current climate (isothermality, maximum temperature of the
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warmest month, annual temperature range, precipitation seasonality, and precipitation of the
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coldest quarter), shown for different regularization parameters: (a) 1, (b) 2.5; and (c) 3. The best
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supported model used a regularization multiplier of 2.5 (b), and was used to predict the
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geographic distribution of suitable nesting beaches for Kemp’s ridley sea turtles under past and
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future climate scenarios.
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Methods S2: Exploring how the current and past/future climates differ. I explored how novel
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climatic conditions (those outside of the current range for reconstructed past and predicted future
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climates) influenced Kemp’s ridley nesting predictions in three ways. First, I examined how
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MaxEnt predictions change when novel climate variables (i.e., those outside the current climatic
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conditions) are restricted to the current environmental limits (i.e., “clamping” the data). I did this
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by mapping the absolute difference in MaxEnt predictions when clamping was applied or not.
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Second, I used a multivariate similarity surface (MESS; Elith et al. 2010), which depicts how
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similar each grid cell is to conditions experienced during model training, and thus how far
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climatic conditions were within or outside of the range of current climatic conditions. This metric
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can be positive (i.e., climatic conditions are within the range of current conditions) or negative (at
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least one climatic variable is outside of the range of the reference climate space), and as this score
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approaches 100 the grid cell becomes closer to the median value for the current climatic
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conditions. Third, for each grid cell in each climate prediction I determined the individual
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variable that was most dissimilar from current climatic conditions (i.e., MoD; Elith et al. 2010).
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Figure S3: Multivariate similarity surface for past climatic conditions (MESS; Elith et al. 2010),
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showing how similar each grid cell is to conditions seen during model training, and thus whether
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climatic conditions were within or outside of the range of current climatic conditions. (a) last
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interglacial, (b) last glacial maximum CSM model, (c) last glacial maximum MIROC model.
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Blue indicates positive values (MESS+; i.e. climatic parameters within the bounds of the
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reference set), and red indicates negative values (MESS−; i.e. at least one climatic parameter has
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a value outside the range of the reference set; novel projection climates).
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Figure S4: Multivariate similarity surface for future predicted climatic conditions (MESS; Elith
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et al. 2010), showing how similar each grid cell is to conditions seen during model training, and
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thus whether climatic conditions were within or outside of the range of current climatic
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conditions as estimated by Maxent models predicted under low (B2a) and high (A2a) future
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climate predictions for the years 2020, 2050, and 2080. Blue indicates positive values (MESS+;
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i.e. climatic parameters within the bounds of the reference set), and red indicates negative values
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(MESS−; i.e. at least one climatic parameter has a value outside the range of the reference set;
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novel projection climates).
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Table S5: Tests of niche similarity between Kemp’s ridley sea turtles estimated under different
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climatic conditions. Shown are three niche similarity indices: I (above and right of the diagonal),
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D (below and left of the diagonal), and relative rank (below and left of the diagonal, in
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parentheses). Climatic conditions are: LIG (Last interglacial); CCSM (last glacial maximum);
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MIROC (last glacial maximum), and future scenarios for the years 2020, 2050, and 2080 under
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high (A2a) and low (B2a) emission scenarios (averaged for different circulation models, as
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described in the Methods). Metrics were calculated using ENMTools (Warren et al. 2010), as
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described by Pike (2013).
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Current
LIG
LGM
A2a_2020
A2a_2050
A2a_2080
B2a_2020
B2a_2050
B2a_2080
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Current
0.98
(0.89)
0.79
(0.92)
0.85
(0.97)
0.85
(0.95)
0.84
(0.94)
0.85
(0.97)
0.85
(0.95)
0.85
(0.94)
LIG
0.95
-
LGM
0.99
0.99
A2a_2020
0.98
0.95
A2a_2050
0.98
0.95
A2a_2080
0.97
0.94
B2a_2020
0.98
0.95
B2a_2050
0.98
0.95
B2a_2080
0.98
0.95
0.98
(0.84)
0.81
(0.89)
0.81
(0.88)
0.79
(0.87)
0.80
(0.88)
0.80
(0.88)
0.80
(0.88)
-
0.99
0.99
0.99
0.99
0.99
0.99
0.99
(0.88)
0.99
(0.86)
0.99
(0.84)
0.99
(0.87)
0.99
(0.87)
0.99
(0.86)
-
0.99
0.99
0.99
0.99
0.99
0.95
(0.97)
0.91
(0.94)
0.98
(0.99)
0.96
(0.97)
0.93
(0.95)
-
0.99
0.99
0.99
0.99
0.94
(0.96)
0.96
(0.97)
0.98
(0.98)
0.96
(0.97)
-
0.99
0.99
0.99
0.91
(0.94)
0.94
(0.95)
0.96
(0.97)
-
0.99
0.99
0.96
(0.97)
0.93
(0.95)
-
0.99
0.96
(0.97)
-
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REFERENCES
Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods in
Ecology and Evolution, 1, 330-342.
Pike DA (2013) Climate influences the global distribution of sea turtle nesting. Global Ecology
and Biogeography, 22, 555-566.
Warren DL, Glor RE, Turelli M (2010) ENMTools: a toolbox for comparative studies of
environmental niche models. Ecography, 33, 607-611.