| 1. |
- Bradter, Ute, et al.
(författare)
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Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains
- 2022
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Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:21, s. 6209-6227
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Tidskriftsartikel (refereegranskat)abstract
- The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under “space-for-time substitution”, the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996–2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.
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| 2. |
- Lehikoinen, Aleksi, et al.
(författare)
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Large-scale climatic drivers of regional winter bird population trends
- 2016
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Ingår i: Diversity and Distributions. - : Wiley. - 1366-9516. ; 22:11, s. 1163-1173
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Tidskriftsartikel (refereegranskat)abstract
- Aim: Changes in climate and land use practices have been found to affect animal populations in different parts of the world. These studies have typically been conducted during the breeding season, whereas the non-breeding season (hereafter ‘winter’) has received much less attention. Changes in regional winter abundances could be caused by changes in overall population sizes and/or redistribution of populations. We tested these mechanisms for terrestrial winter bird population changes in Northern Europe and explored the role of climate change and species habitat preference. Location: The Netherlands, Denmark, Sweden, Finland. Methods: We used winter bird counts from four countries conducted annually between 15 December and 20 January in 1980/1981–2013/2014. We report national population trends for 50 species for which a trend could be calculated in at least three of the countries. We analysed country-specific population growth rates in relation to species’ climatic summer and winter niches, habitat preference and migratory behaviour. Results: Species breeding in colder (typically northern) areas showed more negative winter population trends than species breeding in warmer areas. Regional winter population trends were negatively correlated with characteristics of their winter climatic niche: populations in the colder part of their winter distribution increased in abundance, whereas populations in the warmer part of their winter distribution decreased. Woodland species tended to do better than farmland species. Migratory behaviour did not explain variation in population trends. Main conclusions: The generally decreasing winter population trends of cold-dwelling breeding species probably reflect the general decline in population sizes of these species. In contrast, increasing winter population trends for populations in the colder parts of the winter distribution indicate a redistribution of wintering individuals towards the north-east. Both these patterns are likely caused by climate change.
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| 3. |
- Piirainen, Sirke, et al.
(författare)
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Species distributions models may predict accurately future distributions but poorly how distributions change : A critical perspective on model validation
- 2023
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Ingår i: Diversity and Distributions. - : Wiley. - 1366-9516 .- 1472-4642. ; 29:5, s. 654-665
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Tidskriftsartikel (refereegranskat)abstract
- Aim: Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance. Location: Fennoscandia. Methods: We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013–2016, ‘static validation’) and for a change between two time periods (difference between 1996–1999 and 2013–2016, ‘change validation’). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits. Results: Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait. Main Conclusions: Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.
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