Large‐scale distribution of tuna species in a warming ocean

Tuna are globally distributed species of major commercial importance and some tuna species are a major source of protein in many countries. Tuna are characterized by dynamic distribution patterns that respond to climate variability and long‐term change. Here, we investigated the effect of environmental conditions on the worldwide distribution and relative abundance of six tuna species between 1958 and 2004 and estimated the expected end‐of‐the‐century changes based on a high‐greenhouse gas concentration scenario (RCP8.5). We created species distribution models using a long‐term Japanese longline fishery dataset and two‐step generalized additive models. Over the historical period, suitable habitats shifted poleward for 20 out of 22 tuna stocks, based on their gravity centre (GC) and/or one of their distribution limits. On average, tuna habitat distribution limits have shifted poleward 6.5 km per decade in the northern hemisphere and 5.5 km per decade in the southern hemisphere. Larger tuna distribution shifts and changes in abundance are expected in the future, especially by the end‐of‐the‐century (2080–2099). Temperate tunas (albacore, Atlantic bluefin, and southern bluefin) and the tropical bigeye tuna are expected to decline in the tropics and shift poleward. In contrast, skipjack and yellowfin tunas are projected to become more abundant in tropical areas as well as in most coastal countries' exclusive economic zones (EEZ). These results provide global information on the potential effects of climate change in tuna populations and can assist countries seeking to minimize these effects via adaptive management.     

Projections of climate‐driven changes in tuna vertical habitat based on species‐specific differences in blood oxygen affinity

Oxygen concentrations are hypothesized to decrease in many areas of the ocean as a result of anthropogenically driven climate change, resulting in habitat compression for pelagic animals. The oxygen partial pressure, pO₂, at which blood is 50% saturated (P₅₀) is a measure of blood oxygen affinity and a gauge of the tolerance of animals for low ambient oxygen. Tuna species display a wide range of blood oxygen affinities (i.e., P₅₀ values) and therefore may be differentially impacted by habitat compression as they make extensive vertical movements to forage on subdaily time scales. To project the effects of end‐of‐the‐century climate change on tuna habitat, we calculate tuna P₅₀ depths (i.e., the vertical position in the water column at which ambient pO₂ is equal to species‐specific blood P₅₀ values) from 21st century Earth System Model (ESM) projections included in the fifth phase of the Climate Model Intercomparison Project (CMIP5). Overall, we project P₅₀ depths to shoal, indicating likely habitat compression for tuna species due to climate change. Tunas that will be most impacted by shoaling are Pacific and southern bluefin tunas—habitat compression is projected for the entire geographic range of Pacific bluefin tuna and for the spawning region of southern bluefin tuna. Vertical shifts in P₅₀ depths will potentially influence resource partitioning among Pacific bluefin, bigeye, yellowfin, and skipjack tunas in the northern subtropical and eastern tropical Pacific Ocean, the Arabian Sea, and the Bay of Bengal. By establishing linkages between tuna physiology and environmental conditions, we provide a mechanistic basis to project the effects of anthropogenic climate change on tuna habitats.     

中西太平洋鲣鱼围网渔业资源的热点分析和空间异质性

中西太平洋是世界鲣鱼围网主要作业水域。基于我国渔船2005—2009年的中西太平洋鲣鱼围网生产数据,运用空间统计方法对该水域鲣鱼资源的空间自相关性和空间异质性特征进行分析,并结合海洋环境特征分析资源分布的热点区域。(1)通过常规统计学计算获得鲣鱼资源的偏态Sk、峰态数Ku、变异值Cv、s2/m和全局空间自相关Geary c系数,发现中西太平洋鲣鱼资源总体上是以低密度区域为主,高密度区域较少;鱼类资源密度值差异较大,资源表现出强烈集聚分布,总体的空间自相关性中等偏弱。(2)通过局部空间自相关的热点分析方法计算,发现局部空间自相关性较强,存在多个在统计学上通过显著性检验的资源热点和冷点。(3)通过地统计方法研究鲣鱼资源的空间变异性特征和方向变异时,空间自相关类型上最优模型是球形模型,鲣鱼资源密度各向同性,最大相关距离1000km左右。发现空间自相关引起的差异占整个差异的50%左右,为中等强度变异;在方向性变异上,主要体现在南北向上,其该向上结构性误差占67%,而东西向结构性误差占49%。这一结果和海洋环境的南北向上结构性远好于东西向结构性有关;从各方向的分维数看,数值介于1.876—1.9之间,数值较大,空间自相关较弱。(4)以资源热点区域作为区域性渔场,结合海洋温度和叶绿素场海洋环境特征,将中西太平洋鲣鱼资源分为3个不同的局部渔场,即2个暖池渔场,1个冷舌渔场。冷舌渔场由中东太平洋赤道上升流引起,在锋面地带提供了较为丰富的初级生产力,便于鱼类获得丰富的食物;暖池渔场靠近岛屿和陆地区域,近岸上升流系统提供了丰富的初级生产力。(5)将热点分析和渔场重心方法及栖息地指数的优缺点做了对比,建议以后采用空间残差模型深入研究空间自相关问题。     

Large‐scale climatic variability affects the dynamics of tropical skipjack tuna in the Western Pacific Ocean

The role of climate variability in determining the fluctuations of fish populations had been a traditional problem in ecology. In this paper, we studied the role of the Southern Oscillation Index (SO) and the Pacific Decadal Oscillation (PDO) on the population dynamics of the western stock of the skipjack tuna Katsuwonus pelamis. Our analysis was based in three sequential steeps: a diagnostic approach to deduce what kind of population dynamic model should be more appropriate, the modelling of capture per unit of effort data through a logistic model, and the use of population dynamic theory for analyzing the effect of exogenous perturbations. We find that direct and one‐year lagged negative PDO effects and one‐year lagged negative SO effects were needed to explain annual tuna fluctuations. Models including the combined effects of these climatic indexes explain 80% of the variance in tuna fluctuations. In addition, these models provided very accurate predictions of independent skipjack tuna observed dynamics. This result is encouraging because the inherent variability in CPUE data and the not well determined link between climate and ecological processes. Finally, this study demonstrates that simple models can offer reasonable explanations and accurate predictions of tuna fluctuations, provided they are based on a sound theoretical framework.     

Spatial and seasonal distribution of the tuna catch on floating objects in the eastern pacific ocean during 1992–1993

An analysis of the catch associated with floating objects by the Mexican tuna purse‐seine fleet in the eastern Pacific Ocean during 1992–1993 was made to determine the spatial and seasonal distribution. The information used was generated by observers of the Programa Nacional de Aprovechamiento del Atun y Protección a los Delfines (PNAAPD). There was no clear seasonal and spatial distribution of floating objects examined in this study, however there were areas where floating objects were more common; the mouth of the Gulf of California, waters offshore Peru, and in oceanic waters. The largest catch of yellowfin tuna was offshore of Peru in winter. Two areas with largest (length) yellowfin tuna were the mouth of the Gulf of California and offshore Peru. For skipjack tuna, the largest catch was offshore Peru in winter, but the largest skipjack were caught between 120° and 130°W along 10°N in spring. The largest yellowfin tuna were captured by sets on bamboo, fish aggregating devices (FADs), planks and boards, and logs (trees or parts). The largest skipjack were captured by sets on dead whales, kelp paddies, planks and boards, and pallets and crates. Most of the sets were made during the early hours of the day but an important number of log sets were made in the early afternoon. For the period analyzed, floating objects were more frequent during fall and winter with the area offshore of Peru the most important.     

How do species respond to climate change along an elevation gradient? A case study of the grey‐headed robin (Heteromyias albispecularis)

Climate is predicted to change rapidly in the current century, which may lead to shifts of species' ranges, reduced populations and extinctions. Predicting the responses of species abundance to climate change can provide valuable information to quantify climate change impacts and inform their management and conservation, but most studies have been limited to changes in habitat area due to a lack of abundance data. Here, we use generalized linear model and Bayesian information criteria to develop a predictive model based on the abundance of the grey‐headed robin (GHR) and the data of climatic environmental variables. The model is validated by leave‐one‐out cross‐validation and equivalence tests. The responses of GHR abundance, population size and habitat area by elevation are predicted under the current climate and 15 climate change scenarios. The model predicts that when temperature increases, abundance of GHR displays a positive response at high elevation, but a negative response at low elevation. High precipitation at the higher elevations is a limiting factor to GHR and any reduction in precipitation at high elevation creates a more suitable environment, leading to an increase in abundance of GHR, whereas changes in precipitation have little impact at low elevation. The loss of habitat is much more than would otherwise be assumed in response to climate change. Temperature increase is the predominant factor leading to habitat loss, whereas changes in precipitation play a secondary role. When climate changes, the species not only loses part of its habitat but also suffers a loss in its population size in the remaining habitat. Population size declines more than the habitat area under all considered climate change scenarios, which implies that the species might become extinct long before the complete loss of its habitat. This study suggests that some species might experience much more severe impacts from climate change than predicted from models of habitat area alone. Management policies based on predictions of habitat area decline using occurrence data need to be re‐evaluated and alternative measures need to be developed to conserve species in the face of rapid climate change.     

Consequences of the historical demography on the global population structure of two highly migratory cosmopolitan marine fishes: the yellowfin tuna (<Emphasis Type="Italic">Thunnus albacares</Emphasis>) and the skipjack tuna (<Emphasis Type="Italic">Katsuwonus pelamis</Emphasis>)

Background  

Yellowfin and skipjack tuna are globally distributed in the world's tropical and sub-tropical oceans. Since little, if any, migration of these fishes occurs between the Atlantic and Indo-Pacific Oceans, one might expect to see genetic differences between sub-populations in these ocean basins. However, yellowfin and skipjack tuna have extremely large population sizes. Thus, the rate of genetic drift should be slower than that observed for other tunas.     

Net biome production of the Amazon Basin in the 21st century

Global change includes multiple stressors to natural ecosystems ranging from direct climate and land‐use impacts to indirect degradation processes resulting from fire. Humid tropical forests are vulnerable to projected climate change and possible synergistic interactions with deforestation and fire, which may initiate a positive feedback to rising atmospheric CO₂. Here, we present results from a multifactorial impact analysis that combined an ensemble of climate change models with feedbacks from deforestation and accidental fires to quantify changes in Amazon Basin carbon cycling. Using the LPJmL Dynamic Global Vegetation Model, we modelled spatio‐temporal changes in net biome production (NBP); the difference between carbon fluxes from fire, deforestation, soil respiration and net primary production. By 2050, deforestation and fire (with no CO₂ increase or climate change) resulted in carbon losses of 7.4–20.3 Pg C with the range of uncertainty depending on socio‐economic storyline. During the same time period, interactions between climate and land use either compensated for carbon losses due to wetter climate and CO₂ fertilization or exacerbated carbon losses from drought‐induced forest mortality (?20.1 to +4.3 Pg C). By the end of the 21st century, depending on climate projection and the rate of deforestation (including its interaction with fire), carbon stocks either increased (+12.6 Pg C) or decreased (?40.6 Pg C). The synergistic effect of deforestation and fire with climate change contributed up to 26–36 Pg C of the overall decrease in carbon stocks. Agreement between climate projections (n=9), not accounting for deforestation and fire, in 2050 and 2098 was relatively low for the directional change in basin‐wide NBP (19–37%) and aboveground live biomass (13–24%). The largest uncertainty resulted from climate projections, followed by implementation of ecosystem dynamics and deforestation. Our analysis partitions the drivers of tropical ecosystem change and is relevant for guiding mitigation and adaptation policy related to global change.     

The role of demography,intra‐species variation,and species distribution models in species' projections under climate change

Organisms are projected to shift their distribution ranges under climate change. The typical way to assess range shifts is by species distribution models (SDMs), which predict species’ responses to climate based solely on projected climatic suitability. However, life history traits can impact species’ responses to shifting habitat suitability. Additionally, it remains unclear if differences in vital rates across populations within a species can offset or exacerbate the effects of predicted changes in climatic suitability on population viability. In order to obtain a fuller understanding of the response of one species to projected climatic changes, we coupled demographic processes with predicted changes in suitable habitat for the monocarpic thistle Carlina vulgaris across northern Europe. We first developed a life history model with species‐specific average fecundity and survival rates and linked it to a SDM that predicted changes in habitat suitability through time with changes in climatic variables. We then varied the demographic parameters based upon observed vital rates of local populations from a translocation experiment. Despite the fact that the SDM alone predicted C. vulgaris to be a climate ‘winner’ overall, coupling the model with changes in demography and small‐scale habitat suitability resulted in a matrix of stable, declining, and increasing patches. For populations predicted to experience declines or increases in abundance due to changes in habitat suitability, altered fecundity and survival rates can reverse projected population trends.     

Predicting differential effects of climate change at the population level with life-cycle models of spring Chinook salmon

Habitat conditions mediate the effects of climate, so neighboring populations with differing habitat conditions may differ in their responses to climate change. We have previously observed that juvenile survival in Snake River spring/summer Chinook salmon is strongly correlated with summer temperature in some populations and with fall streamflow in others. Here, we explore potential differential responses of the viability of four of these populations to changes in streamflow and temperature that might result from climate change. First, we linked predicted changes in air temperature and precipitation from several General Circulation Models to a local hydrological model to project streamflow and air temperature under two climate‐change scenarios. Then, we developed a stochastic, density‐dependent life‐cycle model with independent environmental effects in juvenile and ocean stages, and parameterized the model for each population. We found that mean abundance decreased 20–50% and the probability of quasi‐extinction increased dramatically (from 0.1–0.4 to 0.3–0.9) for all populations in both scenarios. Differences between populations were greater in the more moderate climate scenario than in the more extreme, hot/dry scenario. Model results were relatively robust to realistic uncertainty in freshwater survival parameters in all scenarios. Our results demonstrate that detailed population models can usefully incorporate climate‐change predictions, and that global warming poses a direct threat to freshwater stages in these fish, increasing their risk of extinction. Because differences in habitat may contribute to the individualistic population responses we observed, we infer that maintaining habitat diversity will help buffer some species from the impacts of climate change.     

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species‐specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model‐data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.     

Fast versus slow growing tuna species: age,growth, and implications for population dynamics and fisheries management

Growth models describe the change in length or weight as a function of age. Growth curves in tunas can take different forms from relatively simple von Bertalanffy growth curves (Atlantic bluefin, albacore tunas) to more complex two- or three-stanza growth curves (yellowfin, bigeye, skipjack, southern bluefin tunas). We reviewed the growth of the principal market tunas (albacore, bigeye, skipjack, yellowfin and the three bluefin tuna species) in all oceans to ascertain the different growth rates among tuna species and their implications for population productivity and resilience. Tunas are among the fastest-growing of all fishes. Compared to other species, tunas exhibit rapid growth (i.e., relatively high K) and achieve large body sizes (i.e., high L _∞ ). A comparison of their growth functions reveals that tunas have evolved different growth strategies. Tunas attain asymptotic sizes (L _∞ ), ranging from 75 cm FL (skipjack tuna) to 400 cm FL (Atlantic bluefin tuna), and reach L _∞ at different rates (K), varying from 0.95 year^?1 (skipjack tuna) to 0.05 year^?1 (Atlantic bluefin tuna). Skipjack tuna (followed by yellowfin tuna) is considered the “fastest growing” species of all tunas. Growth characteristics have important implications for population dynamics and fisheries management outcomes since tunas, and other fish species, with faster growth rates generally support higher estimates of Maximum Sustainable Yield (MSY) than species with slower growth rates.     

Linking habitat use to range expansion rates in fragmented landscapes: a metapopulation approach

Temperature increases because of climate change are expected to cause expansions at the high latitude margins of species distributions, but, in practice, fragmented landscapes act as barriers to colonization for most species. Understanding how species distributions will shift in response to climate change therefore requires techniques that incorporate the combined effects of climate and landscape‐scale habitat availability on colonization rates. We use a metapopulation model (Incidence Function Model, IFM) to test effects of fine‐scale habitat use on patterns and rates of range expansion by the butterfly Hesperia comma. At its northern range margin in Britain, this species has increased its breadth of microhabitat use because of climate warming, leading to increased colonization rates. We validated the IFM by reconstructing expansions in five habitat networks between 1982 and 2000, before using it to predict metapopulation dynamics over 100 yr, for three scenarios based on observed changes to habitat use. We define the scenarios as “cold‐world” (only hot, south‐facing 150–250° hillsides are deemed warm enough), “warm‐world” in which 100–300° hillsides can be populated, and “hot‐world”, where the background climate is warm enough to enable use of all aspects (as increasingly observed). In the simulations, increased habitat availability in the hot‐world scenario led to faster range expansion rates, and to long‐term differences in distribution size and pattern. Thus, fine‐scale changes in the distribution of suitable microclimates led to landscape‐scale changes in population size and colonization rate, resulting in coarse‐scale changes to the species distribution. Despite use of a wider range of habitats associated with climate change, H. comma is still expected to occupy a small fraction of available habitat in 100 yr. The research shows that metapopulation models represent a potential framework to identify barriers to range expansion, and to predict the effects of environmental change or conservation interventions on species distributions and persistence.     

Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming?

Models that couple habitat suitability with demographic processes offer a potentially improved approach for estimating spatial distributional shifts and extinction risk under climate change. Applying such an approach to five species of Australian plants with contrasting demographic traits, we show that: (i) predicted climate‐driven changes in range area are sensitive to the underlying habitat model, regardless of whether demographic traits and their interaction with habitat patch configuration are modeled explicitly; and (ii) caution should be exercised when using predicted changes in total habitat suitability or geographic extent to infer extinction risk, because the relationship between these metrics is often weak. Measures of extinction risk, which quantify threats to population persistence, are particularly sensitive to life‐history traits, such as recruitment response to fire, which explained approximately 60% of the deviance in expected minimum abundance. Dispersal dynamics and habitat patch structure have the strongest influence on the amount of movement of the trailing and leading edge of the range margin, explaining roughly 40% of modeled structural deviance. These results underscore the need to consider direct measures of extinction risk (population declines and other measures of stochastic viability), as well as measures of change in habitat area, when assessing climate change impacts on biodiversity. Furthermore, direct estimation of extinction risk incorporates important demographic and ecosystem processes, which potentially influence species’ vulnerability to extinction due to climate change.     

Identifying refugia and corridors under climate change conditions for the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) in Hubei Province,China

Using a case study of an isolated management unit of Sichuan snub‐nosed monkey (Rhinopithecus roxellana), we assess the extent that climate change will impact the species’ habitat distribution in the current period and projected into the 2050s. We identify refugia that could maintain the population under climate change and determine dispersal paths for movement of the population to future suitable habitats. Hubei Province, China. We identified climate refugia and potential movements by integrating bioclimatic models with circuit theory and least‐cost model for the current period (1960–1990) and the 2050s (2041–2060). We coupled a maximum entropy algorithm to predict suitable habitat for the current and projected future periods. Suitable habitat areas that were identified during both time periods and that also satisfied home range and dispersal distance conditions were delineated as refugia. We mapped potential movements measured as current flow and linked current and future habitats using least‐cost corridors. Our results indicate up to 1,119 km² of currently suitable habitat within the study range. Based on our projections, a habitat loss of 67.2% due to climate change may occur by the 2050s, resulting in a reduced suitable habitat area of 406 km² and very little new habitat. The refugia areas amounted to 286 km² and were located in Shennongjia National Park and Badong Natural Reserve. Several connecting corridors between the current and future habitats, which are important for potential movements, were identified. Our assessment of the species predicted a trajectory of habitat loss following anticipated future climate change. We believe conservation efforts should focus on refugia and corridors when planning for future species management. This study will assist conservationists in determining high‐priority regions for effective maintenance of the endangered population under climate change and will encourage increased habitat connectivity.     

Evaluating ecosystem effects of climate change on tropical island streams using high spatial and temporal resolution sampling regimes

Climate change is expected to alter precipitation patterns worldwide, which will affect streamflow in riverine ecosystems. It is vital to understand the impacts of projected flow variations, especially in tropical regions where the effects of climate change are expected to be one of the earliest to emerge. Space‐for‐time substitutions have been successful at predicting effects of climate change in terrestrial systems by using a spatial gradient to mimic the projected temporal change. However, concerns have been raised that the spatial variability in these models might not reflect the temporal variability. We utilized a well‐constrained rainfall gradient on Hawaii Island to determine (a) how predicted decreases in flow and increases in flow variability affect stream food resources and consumers and (b) if using a high temporal (monthly, four streams) or a high spatial (annual, eight streams) resolution sampling scheme would alter the results of a space‐for‐time substitution. Declines in benthic and suspended resource quantity (10‐ to 40‐fold) and quality (shift from macrophyte to leaf litter dominated) contributed to 35‐fold decreases in macroinvertebrate biomass with predicted changes in the magnitude and variability in the flow. Invertebrate composition switched from caddisflies and damselflies to taxa with faster turnover rates (mosquitoes, copepods). Changes in resource and consumer composition patterns were stronger with high temporal resolution sampling. However, trends and ranges of results did not differ between the two sampling regimes, indicating that a suitable, well‐constrained spatial gradient is an appropriate tool for examining temporal change. Our study is the first to investigate resource to community wide effects of climate change on tropical streams on a spatial and temporal scale. We determined that predicted flow alterations would decrease stream resource and consumer quantity and quality, which can alter stream function, as well as biomass and habitat for freshwater, marine, and terrestrial consumers dependent on these resources.     

Ecological costs of climate change on marine predator–prey population distributions by 2050

Identifying and quantifying the effects of climate change that alter the habitat overlap of marine predators and their prey population distributions is of great importance for the sustainable management of populations. This study uses Bayesian joint models with integrated nested Laplace approximation (INLA) to predict future spatial density distributions in the form of common spatial trends of predator–prey overlap in 2050 under the “business‐as‐usual, worst‐case” climate change scenario. This was done for combinations of six mobile marine predator species (gray seal, harbor seal, harbor porpoise, common guillemot, black‐legged kittiwake, and northern gannet) and two of their common prey species (herring and sandeels). A range of five explanatory variables that cover both physical and biological aspects of critical marine habitat were used as follows: bottom temperature, stratification, depth‐averaged speed, net primary production, and maximum subsurface chlorophyll. Four different methods were explored to quantify relative ecological cost/benefits of climate change to the common spatial trends of predator–prey density distributions. All but one future joint model showed significant decreases in overall spatial percentage change. The most dramatic loss in predator–prey population overlap was shown by harbor seals with large declines in the common spatial trend for both prey species. On the positive side, both gannets and guillemots are projected to have localized regions with increased overlap with sandeels. Most joint predator–prey models showed large changes in centroid location, however the direction of change in centroids was not simply northwards, but mostly ranged from northwest to northeast. This approach can be very useful in informing the design of spatial management policies under climate change by using the potential differences in ecological costs to weigh up the trade‐offs in decisions involving issues of large‐scale spatial use of our oceans, such as marine protected areas, commercial fishing, and large‐scale marine renewable developments.     

Effect of prey availability on the abundance of White‐breasted Wood‐Wrens,insectivorous birds of tropical lowland forests

Some understory insectivorous birds manage to persist in tropical forest fragments despite significant habitat loss and forest fragmentation. Their persistence has been related to arthropod biomass. In addition, forest structure has been used as a proxy to estimate prey availability for understory birds and for calculating prey abundance. We used arthropod biomass and forest structural variables (leaf area index [LAI] and aerial leaf litter biomass) to explain the abundance of White‐breasted Wood‐Wrens (Henicorhina leucosticta), tropical understory insectivorous birds, in six forests in the Caribbean lowlands of Costa Rica. To estimate bird abundance, we performed point counts (100‐m radius) in two old‐growth forests, two second‐growth forests, and two selectively logged forests. Arthropod abundance was the best predictor of wood‐wren abundance (wi = 0.75). Wood‐wren abundance increased as the number of arthropods increased, and the estimated range of bird abundance obtained from the model varied from 0.51 (0.28 – 0.93 [95%CI]) to 3.70 (1.68 – 5.20 [95%CI]) within sites. LAI was positively correlated to prey abundance (P = 0.01), and explained part of the variation in wood‐wren abundance. In forests with high LAI, arthropods have more aerial leaf litter as potential habitat so more potential prey are available for wood‐wrens. Forests with a greater abundance of aerial leaf litter arthropods were more likely to sustain higher densities of wood‐wrens in a fragmented tropical landscape.     

Anticipated climate and land‐cover changes reveal refuge areas for Borneo's orang‐utans

Habitat loss and climate change pose a double jeopardy for many threatened taxa, making the identification of optimal habitat for the future a conservation priority. Using a case study of the endangered Bornean orang‐utan, we identify environmental refuges by integrating bioclimatic models with projected deforestation and oil‐palm agriculture suitability from the 1950s to 2080s. We coupled a maximum entropy algorithm with information on habitat needs to predict suitable habitat for the present day and 1950s. We then projected to the 2020s, 2050s and 2080s in models incorporating only land‐cover change, climate change or both processes combined. For future climate, we incorporated projections from four model and emission scenario combinations. For future land cover, we developed spatial deforestation predictions from 10 years of satellite data. Refuges were delineated as suitable forested habitats identified by all models that were also unsuitable for oil palm – a major threat to tropical biodiversity. Our analyses indicate that in 2010 up to 260 000 km² of Borneo was suitable habitat within the core orang‐utan range; an 18–24% reduction since the 1950s. Land‐cover models predicted further decline of 15–30% by the 2080s. Although habitat extent under future climate conditions varied among projections, there was majority consensus, particularly in north‐eastern and western regions. Across projections habitat loss due to climate change alone averaged 63% by 2080, but 74% when also considering land‐cover change. Refuge areas amounted to 2000–42 000 km² depending on thresholds used, with 900–17 000 km² outside the current species range. We demonstrate that efforts to halt deforestation could mediate some orang‐utan habitat loss, but further decline of the most suitable areas is to be expected given projected changes to climate. Protected refuge areas could therefore become increasingly important for ongoing translocation efforts. We present an approach to help identify such areas for highly threatened species given environmental changes expected this century.     

Large-scale spatial and temporal dynamics of the vulnerable and highly mobile superb parrot

Aim To investigate the spatial and temporal dynamics of the vulnerable and highly mobile superb parrot (Polytelis swainsonii) across its range in south‐eastern mainland Australia. Location South‐eastern Australia (27°–37° S latitude and 141°–151° E longitude). Methods We used generalized additive models (GAMs) to model time‐specific bird atlas occurrence data against time‐specific plant productivity data, plus a range of environmental predictor variables. We then examined the effects of environmental variables on the temporal and spatial patterns of predicted abundance and distribution of the superb parrot using a correlative mapping approach. Results Key findings from GAM analysis were: (1) there was a strong positive relationship between abundance and plant productivity in all regions, but (2) the response of abundance to other predictor variables often differed between regions. Correlative mapping predictions of the abundance and distribution of the superb parrot also indicated that: (1) predicted abundance varied through time and space, (2) predicted abundance sometimes decreased in all regions, but at other times some regions had high abundance when others had low, and (3) changes in plant productivity (and therefore climate) were associated with this variation. Main conclusion The superb parrot favours productive landscapes that are also favoured for agriculture. Movements appear to be associated with seasonal and year to year climate variability. Thus, variation in the recorded abundance of the superb parrot may mask population trends, suggesting that existing population estimates are unreliable. Also, high abundances in some areas, and at some times, may reflect deteriorating habitat conditions elsewhere rather than species recovery. Temporal variability in the distribution of the superb parrot makes it difficult to identify specific drought refugia. Consequently, through time, as key habitat continues to deteriorate, the species will become increasingly vulnerable and threatened. Whole‐landscape habitat conservation and restoration strategies are therefore needed to sustain superb parrot populations in the long‐term.