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Consistent with a warming climate, birds are shifting the timing of their migrations, but it remains unclear to what extent these shifts have kept pace with the changing environment. Because bird migration is primarily cued by annually consistent physiological responses to photoperiod, but conditions at their breeding grounds depend on annually variable climate, bird arrival and climate-driven spring events would diverge. We combined satellite and citizen science data to estimate rates of change in phenological interval between spring green-up and migratory arrival for 48 breeding passerine species across North America. Both arrival and green-up changed over time, usually in the same direction (earlier or later). Although birds adjusted their arrival dates, 9 of 48 species did not keep pace with rapidly changing green-up and across all species the interval between arrival and green-up increased by over half a day per year. As green-up became earlier in the east, arrival of eastern breeding species increasingly lagged behind green-up, whereas in the west—where green-up typically became later—birds arrived increasingly earlier relative to green-up. Our results highlight that phenologies of species and trophic levels can shift at different rates, potentially leading to phenological mismatches with negative fitness consequences.

Climate change is testing the resilience of forests worldwide pushing physiological tolerance to climatic extremes. Plant functional traits have been shown to be adapted to climate and have evolved patterns of trait correlations (similar patterns of distribution) and coordinations (mechanistic trade‐off). We predicted that traits would differentiate between populations associated with climatic gradients, suggestive of adaptive variation, and correlated traits would adapt to future climate scenarios in similar ways. We measured genetically determined trait variation and described patterns of correlation for seven traits: photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), leaf size (LS), specific leaf area (SLA), δ13C (integrated water‐use efficiency, WUE), nitrogen concentration (NCONC), and wood density (WD). All measures were conducted in an experimental plantation on 960 trees sourced from 12 populations of a key forest canopy species in southwestern Australia. Significant differences were found between populations for all traits. Narrow‐sense heritability was significant for five traits (0.15–0.21), indicating that natural selection can drive differentiation; however, SLA (0.08) and PRI (0.11) were not significantly heritable. Generalized additive models predicted trait values across the landscape for current and future climatic conditions (>90% variance). The percent change differed markedly among traits between current and future predictions (differing as little as 1.5% (δ13C) or as much as 30% (PRI)). Some trait correlations were predicted to break down in the future (SLA:NCONC, δ13C:PRI, and NCONC:WD). Synthesis: Our results suggest that traits have contrasting genotypic patterns and will be subjected to different climate selection pressures, which may lower the working optimum for functional traits. Further, traits are independently associated with different climate factors, indicating that some trait correlations may be disrupted in the future. Genetic constraints and trait correlations may limit the ability for functional traits to adapt to climate change. We tested whether functional traits of an important tree species in southwestern Australia were genetically determined and able to undergo selection. Our results suggest that some traits are heritable (Water Use Efficiency) while others are not (SLA), indicating that in the future, under different climates, selection will differentially effect traits. Therefore, it is likely that current trait combinations will change with climate.

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Climate change is increasing the risk of drought to forested ecosystems. Although drought impacts are often anecdotally noted to occur in discrete patches of high canopy mortality, the landscape effects of drought disturbances have received virtually no study. This study characterized the landscape configuration of drought impact patches and investigated the relationships between patch characteristics, as indicators of drought impact intensity, and environmental gradients related to water availability to determine factors influencing drought vulnerability. Drought impact patches were delineated from aerial surveys following an extreme drought in 2011 in southwestern Australia, which led to patchy canopy dieback of the Northern Jarrah Forest, a Mediterranean forest ecosystem. On average, forest gaps produced by drought-induced dieback were moderate in size (6.6 ± 9.7 ha, max = 85.7 ha), compact in shape, and relatively isolated from each other at the scale of several kilometers. However, there was considerable spatial variation in the size, shape, and clustering of forest gaps. Drought impact patches were larger and more densely clustered in xeric areas, with significant relationships observed with topographic wetness index, meteorological variables, and stand height. Drought impact patch clustering was more strongly associated with the environmental factors assessed (R2 = 0.32) than was patch size (R2 = 0.21); variation in patch shape remained largely unexplained (R2 = 0.02). There is evidence that the xeric areas with more intense drought impacts are 'chronic disturbance patches' susceptible to recurrent drought disturbance. The spatial configuration of drought disturbances is likely to influence ecological processes including forest recovery and interacting disturbances such as fire. Regime shifts to an alternate, non-forested ecosystem may occur preferentially in areas with large or clustered drought impact patches. Improved understanding of drought impacts and their patterning in space and time will expand our knowledge of forest ecosystems and landscape processes, informing management of these dynamic systems in an uncertain future.

Determining drivers of species richness is recognised as highly complex, involving many synergies and interactions. We examine the utility of newly available remote sensing representations of vegetation productivity and vegetation structure to examine drivers of species richness at continental and regional scales. We related richness estimates derived from stacked species distribution models for birds, mammals, amphibians, and reptiles to estimates of actual and potential evapotranspiration (AET and PET), forest structure, and forest productivity across Australia as a whole as well as by bioclimatic zones. We used structural equation modeling to partition correlations between climate energy and vegetation attributes and their subsequent associations with species richness. Continentally, vertebrate richness patterns were strongly related to patterns of energy availability. Richness of amphibians, mammals, and birds were positively associated with AET. However, reptile richness was most strongly associated with PET. Regionally, forest structure and productivity associations with bird, mammal, and amphibian richness were strongest. Again, reptile richness associated most strongly with PET. Our results suggest that a hierarchy of drivers of broad-scale vertebrate richness patterns exist (reptiles excluded): 1) climate energy is most important at the continental scale; next, 2) vegetation productivity and vegetation structure are most important at the regional scale; except 3) at low extremes of climate energy when energy becomes limiting.

Aim: Several lines of evidence suggest that beta diversity, or dissimilarity in species composition, should increase with productivity: (1) the latitudinal species richness gradient is most closely related to productivity and associated latitudinal betadiversity relationships have been described, and (2) the scale dependence of the productivity-diversity relationship implies that there should be a positive productivity-beta-diversity relationship. However, such a pattern has not yet been demonstrated at broad scales. We test if there is a gradient of increasing beta diversity with productivity. Location: Canada. Methods: Canada was clustered into regions of similar productivity regimes along three remotely sensed productivity axes (minimum and integrated annual productivity, seasonality of productivity) and elevation. The overall (ß j ), turnover (ß sim ) and nestedness (ß nes ) components of beta diversity within each productivity regime were estimated with pairwise dissimilarity metrics and related to cluster productivity with partial linear regression and with spatial autoregression. Tests were performed for all species, productivity breadth-based subsets (e. g. species occurring in many and a moderate number of productivity regimes), and pre-and post-1970 butterfly records. Beta diversity between adjacent clusters along the productivity gradients was also evaluated. Results: Within-cluster ß j and ß sim increased with productivity and decreased with seasonality. The converse was true for ß nes . All species subsets responded similarly; however, productivity-beta-diversity relationships were weaker for the post-1970 temporal subset and strongest for species of moderate breadth. Between-cluster beta diversity (ß j ) and nestedness (ß nes ) declined with productivity. Main conclusions: As predicted, beta diversity of communities within productivity regimes was observed to increase with productivity. This pattern was driven largely by a gradient of species turnover. Therefore, beta diversity may make an important contribution to the broad-scale gradient of species richness with productivity. However, this species richness gradient dominates regional beta diversity between productivity regimes, resulting in decreasing between-productivity dissimilarity with productivity driven by a concurrent decline in nestedness.

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Many invasive species are too widespread to realistically eradicate. For such species, a viable management strategy is to slow the rate of spread. However, to be effective, this will require detailed spread data and an understanding of the influence of environmental conditions and landscape structure on invasion rates. We used a time series of remotely sensed distribution maps and a spatial simulation model to study spread of the invasive Lepidium latifolium (perennial pepperweed) in California's Sacramento-San Joaquin River Delta. L. latifolium is a noxious weed and exhibited rapid, explosive spread. Annual infested area and empirical dispersal kernels were derived from the remotely sensed distributions in order to assess the influence of weather conditions on spread and to parameterize the simulation model. Spread rates and dispersal distances were highest for nascent infestations and in years with wet springs. Simulations revealed that spread rates were more strongly influenced by the length of long-distance dispersal than by temporal variation in its likelihood. It is thus important to capture long-distance dispersal and the conditions that facilitate spread when collecting data to parameterize spread models. Additionally, management actions performed in high-spread years, targeting long-distance recruits, can effectively contain infestations. Corridors were relatively unimportant to spread rates; their effectiveness at enhancing rate of spread was limited by the species' dispersal ability and the time needed to travel through the corridor. In contrast, habitat abundance and shape surrounding the introduction site strongly influenced invasion dynamics. Satellite patches invading large areas of invasible habitat present especially high risk.

Boreal forests maintain regionally important biodiversity and globally important ecosystem services, such as carbon storage and freshwater resources. Many boreal systems have limited anthropogenic disturbances and are preserved, in effect, to date largely by their harsh climates and remoteness. As of 2011, almost 10% of Canada is subject to some manner of formal protection, with 4.5% of this protected area found within the boreal zone. The management of existing parks and protected areas (PPAs) is shared amongst many federal, provincial, and territorial jurisdictions. Although there are currently low levels of anthropogenic development in some portions of the boreal zone (especially the north), if expansion of protected areas is of interest, there are challenges to traditional PPA networks that may be more prominent in the boreal zone than elsewhere: (1) the boreal zone is home to charismatic mammal species with area requirements much larger than typical PPAs; (2) the boreal zone is characterized by natural disturbance regimes that impact large areas; and (3) projected changes to climate for the boreal zone are among the greatest in the world, creating temporal considerations for conservation planning exercises. There is currently no PPA assessment specific to boreal Canada. To address this lack of an assessment, we developed a conservation gap analysis of the current PPA system with respect to a variety of environmental surrogates (ecozones, land cover, vegetation productivity, and landscape structure). The amount of formally protected land varied within each surrogate, with few commonly reported features meeting national or international conservation targets. Furthermore, few reserves met the areal requirements that have been previously recommended to protect large mammals or accommodate the disturbance regimes present. We also discuss considerations and implications of area-based versus value-based protection objectives. While recognizing that there are still scientific challenges around understanding and evaluating the effectiveness of PPAs, based upon our review and assessment, the following considerations should inform conservation options for the boreal zone: (1) representation of the distribution of natural features within the PPA network; (2) effective maintenance of habitat requirements and spatial resilience to both cyclical and directional changes in spatial patterns through large, connected reserves; and (3) implementation of sustainable forest management practices (where applicable) throughout the broader landscape, as traditional on-reserve protection is unlikely to be sufficient to meet conservation goals. The Canadian boreal is unique in possessing large tracts of inaccessible forested lands that are not subject to management interventions, thereby offering functions similar to protected lands. The question of how to more formally integrate these lands into the existing PPA network requires further consideration. Further, the important temporal role of landscape dynamics in designing an effective PPA needs to be further studied as well as development of a better understanding of design needs in the context of a changing climate.

Ecological and conservation research has provided a strong scientific underpinning to the modeling of ecosystem services (ESs) over space and time, by identifying the ecological processes and components of biodiversity (ecosystem service providers, functional traits) that drive ES supply. Despite this knowledge, efforts to map the distribution of ESs often rely on simple spatial surrogates that provide incomplete and non-mechanistic representations of the biophysical variables they are intended to proxy. However, alternative data sets are available that allow for more direct, spatially nuanced inputs to ES mapping efforts. Many spatially explicit, quantitative estimates of biophysical parameters are currently supported by remote sensing, with great relevance to ES mapping. Additional parameters that are not amenable to direct detection by remote sensing may be indirectly modeled with spatial environmental data layers. We review the capabilities of modern remote sensing for describing biodiversity, plant traits, vegetation condition, ecological processes, soil properties, and hydrological variables and highlight how these products may contribute to ES assessments. Because these products often provide more direct estimates of the ecological properties controlling ESs than the spatial proxies currently in use, they can support greater mechanistic realism in models of ESs. By drawing on the increasing range of remote sensing instruments and measurements, data sets appropriate to the estimation of a given ES can be selected or developed. In so doing, we anticipate rapid progress to the spatial characterization of ecosystem services, in turn supporting ecological conservation, management, and integrated land use planning.