Climate Change: Identification and Projections
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Ongoing changes in climate affect ecosystem composition, structure, and function 1 , 2 , 3 , and paleorecords clearly indicate a high sensitivity of species and ecosystem distributions to climate change 4 , 5. Considerations of the ecological effects of future climate change create challenges for traditional conservation planning 6 , 7 , 8 , 9 , Conservation strategies focused on either restoration or preservation are being adjusted in light of climate impacts For instance, restoration ecologists increasingly consider future climatic conditions in planning 11 , and ecological reserves are proposed that consider projected impacts of climate change In addition, climate change and other human-caused stressors have resulted in calls to adjust management strategies in protected areas Faced with these challenges, climate adaptation heuristics to support conservation decisions have been developed using assessments of existing ecological conditions while considering the predicted or observed impacts of climate change 14 , 15 , 16 , 17 Fig.
These heuristic frameworks can guide management decisions, while also providing conceptual foundations to support mapped data indicating lands where various conservation strategies would reasonably be emphasized 16 , For instance, evaluation of predicted climate change impacts may result in land managers adjusting restoration strategies for areas with degraded ecological conditions.
The historical range of variability may serve as an insufficient target for restoration when considering potential climate change impacts Similarly, decisions on protected areas designation locations or management can be revised based on ongoing or predicted changes in climate 13 , especially when climate-sensitive ecosystems or species occur within boundaries of such conservation reserves.
Whether protected areas may require intensive management intervention, novel management options, or additional flexibility in a climate-altered future remains controversial, especially in relatively intact wildland ecosystems 19 , Climate adaptation heuristics have helped guide thinking on these controversies 10 , 14 , 17 , 21 Fig. Conceptual framework proposed by Belote et al.
Heuristic frameworks and maps typically rely on predictions of multivariate climate change without explicit regard for specific impacts to particular ecosystems or species 16 , Instead, heuristics rely on assumptions that evaluations of changes in multiple dimensions of climate may provide useful guidance on the relative vulnerability of species and ecosystems Climate vulnerability has been described as a function of climate exposure, sensitivity, and adaptive capacity 23 , though we focus here on climate exposure i.
Climate exposure metrics include predictions of changes in individual climate variables 24 , geographic displacement of climate analogues i. Various metrics of predicted exposure suggest that different regions may experience relatively high degrees of climate change depending on which metric is evaluated Evaluating regions where high or low degrees of different exposure metrics coincide may be an important step in evaluating potential future impacts to complex ecosystems.
Heuristics for conservation using mapped indices of climate vulnerability also often focus on the central tendency of climate predictions by using data from multi-model ensembles 16 , median values of simulations 17 , or observed trends in recent historical climate Development of heuristics have been useful, but evaluating uncertainty in climate adaptation conservation planning remains a challenge 30 , 31 , Understanding the uncertainty in climate adaptation planning is critical because it affects confidence in conservation recommendations based on expected changes in climate. Variability in climate change projections can arise through various means including use of different baseline climatological data 33 , emissions scenarios or representative concentration pathways 28 , 34 , 35 , downscaling methods 36 , general circulation models GCMs 37 , choice of climate vulnerability metrics 22 , 26 , 38 , and predicted ecological responses 30 , 32 , 39 , 40 , 41 , 42 , 43 , among others.
Our goal is to evaluate how confidence in conservation recommendations may vary when considering uncertainty in climate projections and variability in metrics of climate exposure, using the contiguous United States as a case study. We used a heuristic framework that recommends conservation strategies based on combinations of existing conservation value and projected climate vulnerability Fig.
To explicitly assess sources of uncertainty and their potential effects on confidence in conservation recommendations, we evaluated variability and agreement in predictions among general circulation models and greenhouse gas concentration scenarios, as well as different climate exposure metrics. We assumed that confidence in conservation recommendations increases with the agreement among climate scenarios and climate exposure metrics.
Our specific objectives were to 1 map three multivariate metrics of climate exposure forward climate velocity, backward climate velocity, and local climate dissimilarity ; 2 map agreement among alternative climate projections for each of the three metrics of exposure; and 3 assess how agreement in climate projections influence confidence in assigning climate adaptation conservation strategies.go here
Climate Change: Identification and Projections
We relied on data available from the AdaptWest project, which is a collaborative effort to disseminate spatial data on predicted changes in climate to aid conservation planners see adaptwest. We used 1-km resolution climate data from AdaptWest 38 , 44 to derive three metrics of climate change exposure at one time step year average centered on Specifically, we mapped estimates of 1 forward climate velocity, 2 backward climate velocity, and 3 local climate dissimilarity.
These metrics have been used in previous studies to evaluate general measures of exposure not focused on any specific species or ecosystem, as is the case of our study 16 , 26 , For our analysis, we focused on multivariate metrics derived from the same data reduction method principal components analysis to emphasize variability derived from alternative climate projections, and not from other methodological decisions i. We also limited our analysis to one time step predictions centered on the year to 1 reduce the complexity of our analysis, 2 focus on alternative predictions holding time constant, and 3 present results for a future timeframe likely to influence land management decisions.
The multi-model ensemble includes some of the eight GCMs and so is not an independent case. However, it is included to represent the common usage of ensemble projections in conservation planning. These nine alternative climate projections allowed us to assess intermodel differences in climate process and parameterization schemes Additionally, we used predictions from two representative concentration pathways RCP of greenhouse gasses i.
Predictions from RCP 4. The purpose of producing these 18 different projections was to generate a range of reasonable climate predictions that represent alternatives available from the AdaptWest project for developing climate-informed conservation planning. We followed Wang et al.
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These variables were transformed to meet assumptions of normality log transform of precipitation and moisture index and square-root transform of degree days and then subjected to a principal components analysis using a correlation matrix approach to reduce the dimensionality of the data. PC1 was most strongly associated with temperature variables, and PC2 was most strongly associated with precipitation and moisture variables. The first two PC scores were used to calculate three multivariate metrics of climate exposure: forward velocity, backward velocity, and local climate dissimilarity.
Forward and backward climate velocities are correlated but distinct 26 , Forward and backward velocity measure the geographic displacement of climate analogues based on the first two PC scores, as a way of assessing the minimum distance organisms would need to travel to track changes in climate 26 but see Forward velocity is based on the distance that current climate conditions average from to are projected to move from their current location into the future, whereas backward velocity measures the distance that climate conditions of the future are projected to have moved to arrive at their locations.
Specifically, velocity estimates were calculated by first binning PC scores to map multivariate climate analogs for current and future time steps. Velocities were then calculated by measuring the minimum distance between climate analogs from current to future time steps forward velocity and future to current time steps backward velocity. Local climate dissimilarity, on the other hand, is a multivariate index that summarizes the magnitude of multivariate climate change expected at each grid cell.
We estimated projected local climate dissimilarity for all grid cell locations by calculating Euclidean distances between current based on average climate between — and future —, i. This method is analogous to the one used by Williams et al. To do this, we classified the climate metric values for all 1-km grid cells into four quartiles and assigned each quartile an integer value of 1 lower quartile to 4 upper quartile.
This quartile assignment was done separately for each of the 18 simulations Supplemental Fig. Then, for each grid cell, we identified which quartile value was most frequently assigned to each grid cell i. The frequency of mode hence serves as an index of model agreement and uncertainty, with values ranging from 5 minimum majority and little agreement among simulations to 18 perfect consensus. Other means to assess uncertainty include calculating the median and the standard deviation of estimates for all grid cell locations.
We also calculated these values Supplemental Fig. By calculating the mode of classified quartiles and the associated frequencies, we could more easily evaluate agreement in simulation predictions in the context of the value and vulnerability maps by which conservation strategies are recommended, compared to assessing variance among simulations. To assess agreement among the three climate metrics, we overlaid the quartile modes across all 18 simulations for each metric and mapped the overlap between the three exposure metrics in the upper and lower quartile see Supplemental Fig. From this map we identified areas of metric agreement in assigning land to either low or high degree of vulnerability.
Our approach is intended to explore differences in maps of conservation recommendations derived from the heuristic of Belote et al. From this analysis, we ask how confident a conservation recommendation assignment would be based on alternative climate predictions. Confidence in recommendations would be greatest where multiple climate projections agree.
To evaluate how the level of agreement among climate projections and metrics influence confidence in assigning conservation strategies, we created bivariate maps using the quartile mode of each climate metric with a map of wildland conservation value as in Belote et al. Wildland conservation value is a composite map based on an assessment of human modification 50 , connectivity between protected areas 51 , and priorities for representing ecosystem 52 and species diversity in conservation reserves By combining maps of this conservation value with future climate vulnerability, Belote et al.
Such classification makes it possible to identify different conservation strategies, ranging from an emphasis on traditional reserve protection in high value-low vulnerability lands, to restoration to historical conditions in low value-low vulnerability locations, to innovative approaches that anticipate and manage for the future in low value-high vulnerability areas High value-high vulnerability areas represent challenging scenarios, whereby protection of conservation values is a priority, but expanded management flexibility may be required to allow interventions under a high degree of climate exposure see 17 for more discussion.
For the map of conservation values, we classified the composite wildland conservation value of Belote et al. We assess regions for assignment of conservation strategies with the highest levels of agreement among alternative climate projections by visually inspecting maps sensu 54 and quantifying the total area within each lower or upper quartile of climate metric and wildland conservation value i.
We focused our attention on locations classified into the highest and lowest quartiles for both conservation value and climate vulnerability i. The resulting bivariate maps show locations with high conservation value-low climate vulnerability; low conservation value-low climate vulnerability; low conservation value-high climate vulnerability; or high conservation value-high climate vulnerability.
Probabilistic climate projections / Climate Analytics
These are the lands where our confidence in assigning a conservation strategy based on values and vulnerability — per the heuristic framework — may be the highest when agreement in climate predictions is high. Specifically, we focus on these areas to remove sources of uncertainty associated with intermediate degrees of values and vulnerability. To assess confidence in the assignment of conservation recommendations, we mapped the level of agreement i. This allowed us to evaluate how the total area of the classified value and vulnerability combinations i.
Patterns of climate vulnerability based on the three metrics forward velocity, backward velocity, and climate dissimilarity varied throughout the country, as did the level of agreement among climate simulations for each metric Fig. For all three metrics, agreement in quartile mode classification was highest in the lower and upper quartiles; less agreement was observed in the middle quartiles.
Understanding Climate Variability and Change
Velocity metrics tended to have higher agreement among projections than did estimates of climate dissimilarity based on frequency of quartile mode see maps and histograms in Fig. Mode of quartile in classified data of three climate vulnerability metrics and frequency of mode among 18 different projections for forward velocity A,B , backward velocity C,D , and climate dissimilarity E,F. Maps B,D,F present an index of inter-simulation uncertainty for each climate metric, with areas of red indicating lower intermodel agreement and higher uncertainty.
The upper Midwestern states were characterized by relatively high degree of climate exposure, whereas mountainous regions of the West and Appalachians were characterized by low forward and backward velocity with relatively high agreement among simulations. All three climate metrics result in low predicted exposure occurring in the coastal range of California, northern and central Arizona, and west Texas Fig. The upper quartile of all three climate metrics occurred in the upper Midwest and deserts and arid regions in the intermountain West.
Areas where two of the three metrics agreed in relatively low or high predictions of exposure were common. Western mountains, the Appalachians, and much the coastal plain of the southeast were characterized by low exposure for at least 2 metrics. The Basin and Range of the West, the upper Midwest, and parts of the Gulf Coast were characterized by high exposure estimates in at least 2 metrics.
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Grid cells with full consensus among metrics classified into the lower quartile mode made up only 2. Maps of inter-metric agreement or disagreement , showing where the mode of three climate metrics forward velocity, backward velocity, and climate dissimilarity were in the lower top map or upper bottom map quartile. The Venn diagram legends show areas where zero, one, two, or all three metrics were assigned an area to the lower or upper quartiles. In the top map, gray indicates areas where no metrics were in the lower quartile, green indicates where only one of the metrics were assigned to the lower quartile mode, light blue areas indicates two metrics had a mode in lower quartile mode, and dark blue indicates that all three metrics were assigned to the lower quartile mode.
In the bottom map, the same pattern is used to map metric agreement using orange one metric assigned to upper quartile mode , red two metrics , and black all three metrics assigned to upper quartile mode. Variability in metrics resulted in different classification of vulnerability in the bivariate framework and maps Fig. Bivariate maps of conservation value and climate vulnerability using three climate metrics: forward velocity A , backward velocity B , and local climate dissimilarity C.
At the lowest threshold 5 out of 18 projections agree , large portions of the upper Midwestern states were classified as low value, high vulnerability for all three metrics. Similarly, many southwestern and western mountains were classified as high value, low vulnerability under all three metrics.
What causes climate variability?
As the standard of agreement was raised to 16, however, the area of agreement dropped markedly. Only metrics of velocity produced a high level of agreement for larger areas in the high value-low vulnerability classification, mainly in the mountainous regions of the West and in the Southern Appalachians. In contrast, climate dissimilarity showed high agreement only in Florida and central Texas.
Maps of locations occupying the four corners of the conceptual framework of Belote et al. Area of each corner is shown in Fig.
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Change in area represented as the proportion of total mapped area of the contiguous U. Arrows along x-axis represent conditions mapped in Fig. Conservation planning increasingly uses climate predictions to inform allocation of strategies 10 , 14 , 16 , 17 , 55 , 56 , Our results confirm previous reports suggesting contrasting geographic patterns among different metrics of climate exposure, though we found a relatively high degree of agreement among the two metrics based on climate velocity metrics.
Even though most regions were characterized by lack of full consensus in agreement among 18 climate projections for each exposure metric, several regions show relatively high degree of agreement in predictions for confidently assigning conservation strategies based on forward and backward velocity. Areas where all three metrics forward velocity, backward velocity, and climate dissimilarity are either low or high were limited to small portions of the country, though at least two of the three metrics did align over relatively large areas.
Mountainous regions throughout the West, for example, were characterized by low degrees of exposure in at least two metrics. These patterns are explained by similarities in the two velocity metrics, which are partially driven by topography.
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Future climate analogues may be in close geographic proximity to existing climate conditions in mountainous regions where steep elevation-driven climate gradients exist 25 , In contrast, areas in the upper Midwest and Great Basin regions were characterized by a high degree of predicted climate exposure for at least two of the three metrics.