Ecography最新文献

A species is expected to be most resilient to environmental change when it occurs across a broad diversity of habitats. However, there is often no visual representation of the past (i.e. prehistoric and historical) context for a species in the range maps published by national and global authorities. Therefore, it is easy to overlook the fact that many species once occupied a broader geographic range, or greater diversity of habitats. Such oversights hinder the effective conservation of species that have become restricted to a subset of their formerly occupied habitats. Here, we quantified the shifted baseline that may underpin some of the ecological misconceptions about species, and developed a rapid assessment method to aid the identification and prioritisation of ‘potential refugee species' (i.e. species that have become restricted to a subset of their formerly occupied niche). The assessment of potential refugee status is different from, but complementary to, the International Union for Conservation of Nature (IUCN) Red List and Green Status frameworks. Our framework defines a continuum of potential refugee status, which was demonstratable in continent-scale maps drawn from biogeographic regionalisation. Applying this framework to all native rodent species across the continent of Australia (a group that has suffered several extinctions and notable declines), we found that the risk of ecological misconceptions caused by shifted baselines (i.e. resulting from ‘shifting baseline syndrome') was prevalent. This suggests that in many cases, translocation opportunities that might be avoided because they are perceived as conservation introductions (as defined by the IUCN translocation guidelines), may in fact fall within the indigenous range, and should therefore be considered reasonable reintroductions. Ultimately, our potential refugee assessment framework will help to facilitate the undertaking of ambitious translocations that will build species' resilience to environmental change by resuming their adaptation to habitats across all formerly occupied bioregions.

The biogeography of irruptive insect herbivores is determined by host availability and climate conditions. As such, outbreak distributions are sensitive to climatic change, especially across large latitudinal gradients. Here, we investigate the outbreak distributions of two understudied defoliators, hemlock sawfly Neodiprion tsugae (Hymenoptera) and western blackheaded budworm Acleris gloverana (Lepidoptera), that have both recently impacted the greatest land area recorded across the Pacific coastal temperate rainforest since the establishment of aerial survey programs. We compiled polygon-based estimates of insect damage collected by aerial observers, forest inventory, and downscaled climatic data to develop gridded estimates of bioclimatic conditions across the extent of the Pacific coastal temperate rainforest, including the continental United States, British Columbia and Alaska. We leveraged these data to develop ensemble machine learning models with the goal of predicting the outbreak distribution of each insect. In this manuscript we: 1) describe the historical patterns of defoliator outbreaks, 2) identify and describe climatic conditions associated with outbreaks in both species and 3) assess whether historic outbreaks have tracked geographic shifts in climate conditions across the region. We demonstrate that outbreaks of hemlock sawfly and western blackheaded budworm have been observed across the Pacific coastal temperature rainforests of North America in each decade since the establishment of the Canadian and United States aerial survey programs. The distribution of outbreaks by both insects were best explained by host availability, a limited range of spring, summer, and winter temperatures, and minimum precipitation. Finally, we demonstrate that outbreaks have tracked the poleward shift in suitable climate over the last century. This study establishes a baseline understanding of the climatic constraints and biogeographic patterns of historic sawfly and budworm outbreaks across the Pacific coastal temperate rainforest and emphasizes the overarching importance of climate in driving the irruptive dynamics of these defoliator species.

Keywords: climate envelope, defoliators, hemlock sawfly, Pacific coastal temperate rainforest, population dynamics, western blackheaded budworm

Mechanisms underlying large-scale spatial patterns of species richness are one of the central issues in ecology. Although contemporary climate, evolutionary history, and historical climate change have been proposed as drivers of species richness patterns, variation in the relative importance of different factors remains a major challenge. Here, using newly compiled distribution data with a spatial resolution of 100 × 100 km for 43 023 angiosperms plant species in east Eurasia, we mapped species richness patterns for plants with different growth forms (i.e. woody versus herbaceous) and range sizes (i.e. wide-ranged versus narrow-ranged species), and compared the relative importance of the four hypotheses in explaining these patterns, i.e. freezing tolerance hypothesis, historical climate change hypothesis, Janzen's hypothesis (predicting that climate seasonality and topography determine species richness patterns), and diversification rate hypothesis. We found that species richness of all angiosperm plants presented a clear latitudinal gradient and was highest in southwestern China and Central Asian mountains. Notably, species richness patterns and their dominant drivers differed between species groups. Historical climate change was the dominant driver for richness patterns of all and herbaceous species. The freezing tolerance hypothesis dominated the drivers for all woody species, while Janzen's hypothesis dominated narrow-ranged woody and herbaceous species. Our study suggests that different hypotheses contribute to large-scale plant richness patterns via their effects on different plant groups. Our results did not support the diversification rate hypothesis, but demonstrated the high importance of historical climate change to plant diversity in east Eurasia, providing new perspectives on the mechanisms of plant diversity patterns in this continent.

Diseases and parasites are important drivers of population dynamics in wild mammal populations. Small and endangered populations that overlap with larger, reservoir populations are particularly vulnerable to diseases and parasites, especially in ecosystems highly influenced by climate change. Sarcoptic mange, caused by a parasitic mite Sarcoptes scabiei, constitutes a severe threat to many wildlife populations and is today considered a panzootic. The Scandinavian arctic fox Vulpes lagopus is endangered with a fragmented distribution and is threatened by e.g. red fox Vulpes vulpes expansion, prey scarcity and inbreeding depression. Moreover, one of the subpopulations in Scandinavia has suffered from repeated outbreaks of sarcoptic mange during the past decade, most likely spread by red foxes. This was first documented in 2013 and then again 2014, 2017, 2019, 2020 and 2021. We used field inventories and wildlife cameras to follow the development of sarcoptic mange outbreaks in this arctic fox subpopulation with specific focus on disease transmission and consequences for reproductive output. In 2013–2014, we documented visual symptoms of sarcoptic mange in about 30% of the total population. Despite medical treatment, we demonstrate demographic consequences where the number of arctic fox litters plateaued and litter size was reduced after the introduction of S. scabiei. Furthermore, we found indications that mange likely was transmitted by a few arctic foxes travelling between several dens, i.e. ‘super-spreaders'. This study highlights sarcoptic mange as a severe threat to small populations and can put the persistence of the entire Scandinavian arctic fox population at risk.

Climate change, landscape homogenization, and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant–pollinator interactions and pollinator's plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant–bee interactions in 68 Scandinavian plant communities in landscapes of varying land-cover heterogeneity along a latitudinal temperature gradient of 4–8°C, and estimated pollen deposition as the number of pollen grains on flowers of the bee-pollinated plants Lotus corniculatus and Vicia cracca. We show that plant–bee interactions, and the pollination potential for these bee-pollinated plants increase with landscape diversity, annual mean temperature, and plant abundance, and decrease with distances to sand-dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant–bee interactions that incorporate pollinator-specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.

Understanding how and why β-diversity varies along latitude is a long-standing challenge in community ecology and rarely addressed in both space and time. We aimed to explore the spatiotemporal variations in macroinvertebrate β-diversity and their underlying drivers in eight biogeographic regions covering a substantial latitudinal gradient of more than 40 degrees. By combining β-diversity partitioning and distance decay of community similarity analyses, we found that subtropical β-diversity varies more in space relative to variation in time compared with temperate β-diversity, as we predicted. This is probably because subtropical β-diversity is shaped by species–environment sorting (SS), caused by habitat heterogeneity and species specialization, more strongly in space relative to time than temperate β-diversity. Our study highlights the importance of SS in shaping latitudinal gradients of β-diversity in space and time.

Plant–plant interactions regulate plant community structure and function. Shifts in these interactions due to global climate change, mediated through disproportional increases of certain species or functional groups, may strongly affect plant community properties. Still, we lack knowledge of community-level effects of climate-driven changes in biotic interactions. We examined plant community interactions by experimentally removing a dominant functional group, graminoids, in semi-natural grasslands in Southern Norway. To test whether the effect of graminoid removal varied with climate, the experiment was replicated across broad-scale temperature and precipitation gradients. To quantify community-level interactions across sites, we tested for changes in the remaining vascular community (i.e. forbs) cover, richness, evenness, and functional traits reflecting leaf-economic investment and plant size over five years. The effect of graminoid removal on forb community structure and functioning varied over time, and along the climate gradients. Forb cover increased in response to graminoid removal, especially at warmer sites. Species richness increased following removal irrespective of climate, whilst evenness increased under warmer and wetter conditions irrespective of removal. No climate or removal effect was found for species turnover. Functional trait responses varied along the precipitation gradient – compared to controls, forb mean SLA decreased in drier conditions after graminoid removal. Leaf thickness increased under cooler and drier conditions irrespective of removal. These community structure alterations demonstrate stronger competitive interactions between forbs and graminoids under warmer conditions, whilst functional trait responses indicate a facilitative effect of graminoids under drier conditions. This indicates that both competition and facilitation regulate plant communities, suggesting complexity when scaling from populations to communities. Finally, both temperature and precipitation determine the direction and intensity of biotic interactions, with ecosystem-wide implications for forb persistence and ecosystem functioning under future climates. Further work is needed to generalise the role of changing interactions in mediating community responses to climate change.

Dengue and yellow fever have complex cycles, involving urban and sylvatic mosquitoes, and non-human primate hosts. To date, efforts to assess the effect of climate change on these diseases have neglected the combination of such crucial factors. Recent studies only considered urban vectors. This is the first study to include them together with sylvatic vectors and the distribution of primates to analyse the effect of climate change on these diseases. We used previously published models, based on machine learning algorithms and fuzzy logic, to identify areas where climatic favourability for the relevant transmission agents could change: 1) favourable areas for the circulation of the viruses due to the environment and to non-human primate distributions; 2) the favourability for urban and sylvatic vectors. We obtained projections of future transmission risk for two future periods and for each disease, and implemented uncertainty analyses to test for predictions reliability. Areas currently favourable for both diseases could keep being climatically favourable, while global favourability could increase a 7% for yellow fever and a 10% increase for dengue. Areas likely to be more affected in the future for dengue include West Africa, South Asia, the Gulf of Mexico, Central America and the Amazon basin. A possible spread of dengue could take place into Europe, the Mediterranean basin, the UK and Portugal; and, in Asia, into northern China. For yellow fever, climate could become more favourable in Central and Southeast Africa; India; and in north and southeast South America, including Brazil, Paraguay, Bolivia, Peru, Colombia and Venezuela. In Brazil, favourability for yellow fever will probably increase in the south, the west and the east. Areas where the transmission risk spread is consistent to the dispersal of vectors are highlighted in respect of areas where the expected spread is directly attributable to environmental changes. Both scenarios could involve different prevention strategies.

Climate change affects biodiversity in a variety of ways, necessitating the exploration of multiple climate dimensions using appropriate metrics. Despite the existence of several climate change metrics tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed ‘climetrics' which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are implemented, including 1) standardized local anomalies; 2) changes in probabilities of local climate extremes; 3) changes in areas of analogous climates; 4) novel climates; 5) changes in distances to analogous climates; and 6) climate change velocity. For climate change velocity, three different algorithms are implemented in the package including; 1) distanced-based velocity (‘dVe'); 2) threshold-based velocity (‘ve'); and 3) gradient-based velocity (‘gVe'). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The 'climetrics' R package is integrated with the 'rts' package for efficient handling of raster time-series data. The functions in 'climetrics' are designed to be user-friendly, making them suitable for less-experienced R users. Detailed descriptions in help pages and vignettes of the package facilitate further customization by advanced users. In summary, the 'climetrics' R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.