Future changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions.
Projecting species’ responses to future climate conditions is critical for anticipating conservation challenges and informing proactive policy and management decisions. However, best practices for choosing climate models for projection ensembles are currently in flux. We compared including a maximum number of models against trimming ensembles based on model validation. This was done within the emerging practice of ensemble building using an increasingly larger number of global climate models (GCMs) for future projections. We used recently reported estimates on primary drivers of population fluctuations for the migratory monarch butterfly (
- NSF-PAR ID:
- 10362233
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect.
To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs.
Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions.
Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.
