When and where to move: Dynamic occupancy models explain the range dynamics of a food nomadic bird under climate and land cover change

Globally, long‐term research is critical to monitor the responses of tropical species to climate and land cover change at the range scale. Citizen science surveys can reveal the long‐term persistence of poorly known nomadic tropical birds occupying fragmented forest patches. We applied dynamic occupancy models to 13 years (2002–2014) of citizen science‐driven presence/absence data on Cape parrot (Poicephalus robustus), a food nomadic bird endemic to South Africa. We modeled its underlying range dynamics as a function of resource distribution, and change in climate and land cover through the estimation of colonization and extinction patterns. The range occupancy of Cape parrot changed little over time (ψ = 0.75–0.83) because extinction was balanced by recolonization. Yet, there was considerable regional variability in occupancy and detection probability increased over the years. Colonizations increased with warmer temperature and area of orchards, thus explaining their range shifts southeastwards in recent years. Although colonizations were higher in the presence of nests and yellowwood trees (Afrocarpus and Podocarpus spp.), the extinctions in small forest patches (≤227 ha) and during low precipitation (≤41 mm) are attributed to resource constraints and unsuitable climatic conditions. Loss of indigenous forest cover and artificial lake/water bodies increased extinction probabilities of Cape parrot. The land use matrix (fruit farms, gardens, and cultivations) surrounding forest patches provides alternative food sources, thereby facilitating spatiotemporal colonization and extinction in the human‐modified matrix. Our models show that Cape parrots are vulnerable to extreme climatic conditions such as drought which is predicted to increase under climate change. Therefore, management of optimum sized high‐quality forest patches is essential for long‐term survival of Cape parrot populations. Our novel application of dynamic occupancy models to long‐term citizen science monitoring data unfolds the complex relationships between the environmental dynamics and range fluctuations of this food nomadic species.     

Tackling intraspecific genetic structure in distribution models better reflects species geographical range

Genetic diversity provides insight into heterogeneous demographic and adaptive history across organisms’ distribution ranges. For this reason, decomposing single species into genetic units may represent a powerful tool to better understand biogeographical patterns as well as improve predictions of the effects of GCC (global climate change) on biodiversity loss. Using 279 georeferenced Iberian accessions, we used classes of three intraspecific genetic units of the annual plant Arabidopsis thaliana obtained from the genetic analyses of nuclear SNPs (single nucleotide polymorphisms), chloroplast SNPs, and the vernalization requirement for flowering. We used SDM (species distribution models), including climate, vegetation, and soil data, at the whole‐species and genetic‐unit levels. We compared model outputs for present environmental conditions and with a particularly severe GCC scenario. SDM accuracy was high for genetic units with smaller distribution ranges. Kernel density plots identified the environmental variables underpinning potential distribution ranges of genetic units. Combinations of environmental variables accounted for potential distribution ranges of genetic units, which shrank dramatically with GCC at almost all levels. Only two genetic clusters increased their potential distribution ranges with GCC. The application of SDM to intraspecific genetic units provides a detailed picture on the biogeographical patterns of distinct genetic groups based on different genetic criteria. Our approach also allowed us to pinpoint the genetic changes, in terms of genetic background and physiological requirements for flowering, that Iberian A. thaliana may experience with a GCC scenario applying SDM to intraspecific genetic units.     

Estimating indices of range shifts in birds using dynamic models when detection is imperfect

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near‐term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.     

Multi‐season occupancy models identify biotic and abiotic factors influencing a recovering Arctic Peregrine Falcon Falco peregrinus tundrius population

Critical information for evaluating the effectiveness of management strategies for species of concern include distinguishing seldom occupied (or low‐quality) habitat from habitat that is frequently occupied and thus contributes substantially to population trends. Using multi‐season models that account for imperfect detection and a long‐term (1981–2002) dataset on migratory Arctic Peregrine Falcons Falco peregrinus tundrius nesting along the Colville River, Alaska, we quantified the effects of previous year's productivity (i.e. site quality), amount of prey habitat, topography, climate, competition and year on occupancy dynamics across two spatial scales (nest‐sites, cliffs) during recovery of the population. Initial occupancy probability was positively correlated with area of surrounding prey habitat and height of nest‐sites above the Colville River. Colonization probability was positively correlated with nest height and negatively correlated with date of snowmelt. Local extinction probability was negatively correlated with productivity, area of prey habitat and nest height. Colonization and local extinction probabilities were also positively and negatively correlated, respectively, with year. Our results suggest that nest‐sites (or cliffs) along the Colville River do not need equal protection measures. Nest‐sites and cliffs with historically higher productivity were occupied most frequently and had lower probability of local extinction. These sites were on cliffs high above the river drainage, surrounded by adequate prey habitat and with southerly aspects associated with early snowmelt and warmer microclimates in spring. Protecting these sites is likely to encourage continued occupancy by Arctic Peregrine Falcons along the Colville River and other similar areas. Our findings also illustrate the importance of evaluating fitness parameters along with climate and habitat features when analysing occupancy dynamics, particularly with a long‐term dataset spanning a range of annual climate variation.     

Forecasting species range dynamics with process‐explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.     

Incongruent range dynamics between co‐occurring Asian temperate tree species facilitated by life history traits

Postglacial expansion to former range limits varies substantially among species of temperate deciduous forests in eastern Asia. Isolation hypotheses (with or without gene flow) have been proposed to explain this variance, but they ignore detailed population dynamics spanning geological time and neglect the role of life history traits. Using population genetics to uncover these dynamics across their Asian range, we infer processes that formed the disjunct distributions of Ginkgo biloba and the co‐occurring Cercidiphyllum japonicum (published data). Phylogenetic, coalescent, and comparative data suggest that Ginkgo population structure is regional, dichotomous (to west–east refugia), and formed ˜51 kya, resulting from random genetic drift during the last glaciation. This split is far younger than the north–south population structure of Cercidiphyllum (~1.89 Mya). Significant (recent) unidirectional gene flow has not homogenized the two Ginkgo refugia, despite 2Nm > 1. Prior to this split, gene flow was potentially higher, resulting in conflicting support for a priori hypotheses that view isolation as an explanation for the variation in postglacial range limits. Isolation hypotheses (with or without gene flow) are thus not necessarily mutually exclusive due to temporal variation of gene flow and genetic drift. In comparison with Cercidiphyllum, the restricted range of Ginkgo has been facilitated by uncompetitive life history traits associated with seed ecology, highlighting the importance of both demography and lifetime reproductive success when interpreting range shifts.     

Host plant preference and offspring performance of a leaf‐mining moth,Caloptilia fraxinella,on two Fraxinus species

The preference‐performance or ‘mother‐knows‐best’ hypothesis states that female insects choose to oviposit on a host plant that increases the performance of their offspring. This positive link between host plant choice and larval performance is especially important for leaf miners with non‐motile larvae that are entirely dependent upon the oviposition choice of the female for host plant location. Preference and performance of the ash leaf coneroller, Caloptilia fraxinella (Ely) (Lepidoptera: Gracillariidae), a specialist on ash trees, Fraxinus spp. (Oleaceae), were tested in a series of laboratory and field experiments. Female C. fraxinella were exposed to two closely related hosts, black ash, Fraxinus nigra Marshall, and green ash, Fraxinus pennsylvanica Marshall var. subintegerrima (Vahl), in oviposition choice and wind tunnel flight experiments to determine which host is most attractive for oviposition. Caloptilia fraxinella females were inconsistent in host choice, yet performance of larvae was greater on green than black ash. In preference studies, C. fraxinella preferred to oviposit on black ash when leaflets were removed from the tree, but preferred intact green ash over black ash seedlings for oviposition and host location in a wind tunnel. In the field, however, more C. fraxinella visited black ash var. ‘Fallgold’ at leaf flush than green ash at the same sites. Age of the ash leaflet also influences oviposition in this leaf miner and females preferred new over old leaflets for oviposition. Performance of C. fraxinella larvae was evaluated in field and laboratory experiments and was greater on green ash than on black ash in both experiments based on larval survival and development time parameters. The stronger oviposition and host location preference in the field for black ash were not linked to enhanced performance of offspring, as green ash was the superior host, supporting higher larval survival and faster development. A stronger host location preference in the wind tunnel for green ash over black ash, however, suggests that under certain circumstances with this moth species, ‘mother (may) know best’.     

Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution

Understanding species–environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. However, for many species, this baseline information does not exist. With reliable presence data, spatial models that predict geographic ranges and identify environmental processes regulating distribution are a cost‐effective and rapid method to achieve this. Yet these spatial models are lacking for many rare and threatened species, particularly in tropical regions. The harpy eagle (Harpia harpyja) is a Neotropical forest raptor of conservation concern with a continental distribution across lowland tropical forests in Central and South America. Currently, the harpy eagle faces threats from habitat loss and persecution and is categorized as Near‐Threatened by the International Union for the Conservation of Nature (IUCN). Within a point process modeling (PPM) framework, we use presence‐only occurrences with climatic and topographical predictors to estimate current and past distributions and define environmental requirements using Ecological Niche Factor Analysis. The current PPM prediction had high calibration accuracy (Continuous Boyce Index = 0.838) and was robust to null expectations (pROC ratio = 1.407). Three predictors contributed 96% to the PPM prediction, with Climatic Moisture Index the most important (72.1%), followed by minimum temperature of the warmest month (15.6%) and Terrain Roughness Index (8.3%). Assessing distribution in environmental space confirmed the same predictors explaining distribution, along with precipitation in the wettest month. Our reclassified binary model estimated a current range size 11% smaller than the current IUCN range polygon. Paleoclimatic projections combined with the current model predicted stable climatic refugia in the central Amazon, Guyana, eastern Colombia, and Panama. We propose a data‐driven geographic range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements.     

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

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Past and future demographic dynamics of alpine species: limited genetic consequences despite dramatic range contraction in a plant from the Spanish Sierra Nevada

Anthropogenic global climate change is expected to cause severe range contractions among alpine plants. Alpine areas in the Mediterranean region are of special concern because of the high abundance of endemic species with narrow ranges. This study combined species distribution models, population structure analyses and Bayesian skyline plots to trace the past and future distribution and diversity of Linaria glacialis, an endangered narrow endemic species that inhabits summits of Sierra Nevada (Spain). The results showed that: (i) the habitat of this alpine‐Mediterranean species in Sierra Nevada suffered little changes during glacial and interglacial stages of late Quaternary; (ii) climatic oscillations in the last millennium (Medieval Warm Period and Little Ice Age) moderately affected the demographic trends of L. glacialis; (iii) future warming conditions will cause severe range contractions; and (iv) genetic diversity will not diminish at the same pace as the distribution range. As a consequence of the low population structure of this species, genetic impoverishment in the alpine zones of Sierra Nevada should be limited during range contraction. We conclude that maintenance of large effective population sizes via high mutation rates and high levels of gene flow may promote the resilience of alpine plant species when confronted with global warming.     

Phenology predicts the native and invasive range limits of common ragweed

Accurate models for species' distributions are needed to forecast the progress and impacts of alien invasive species and assess potential range‐shifting driven by global change. Although this has traditionally been achieved through data‐driven correlative modelling, robustly extrapolating these models into novel climatic conditions is challenging. Recently, a small number of process‐based or mechanistic distribution models have been developed to complement the correlative approaches. However, tests of these models are lacking, and there are very few process‐based models for invasive species. We develop a method for estimating the range of a globally invasive species, common ragweed (Ambrosia artemisiifolia L.), from a temperature‐ and photoperiod‐driven phenology model. The model predicts the region in which ragweed can reach reproductive maturity before frost kills the adult plants in autumn. This aligns well with the poleward and high‐elevation range limits in its native North America and in invaded Europe, clearly showing that phenological constraints determine the cold range margins of the species. Importantly, this is a ‘forward’ prediction made entirely independently of the distribution data. Therefore, it allows a confident and biologically informed forecasting of further invasion and range shifting driven by climate change. For ragweed, such forecasts are extremely important as the species is a serious crop weed and its airborne pollen is a major cause of allergy and asthma in humans. Our results show that phenology can be a key determinant of species' range margins, so integrating phenology into species distribution models offers great potential for the mechanistic modelling of range dynamics.     

Colonization and persistence ability explain the extent to which plant species fill their potential range

Aim How species traits and environmental conditions affect biogeographical dynamics is poorly understood. Here we test whether estimates of a species’ evolutionary age, colonization and persistence ability can explain its current ‘range filling’ (the ratio between realized and potential range size). Location Fynbos biome (Cape Floristic Region, South Africa). Methods For 37 species of woody plants (Proteaceae), we estimate range filling using atlas data and distribution models, evolutionary age using molecular phylogenies, and persistence ability using estimates of individual longevity (which determines the probability of extinction of local populations). Colonization ability is estimated from validated process‐based seed dispersal models, the arrangement of potential habitat, and data on local abundance. To relate interspecific variation in range filling to evolutionary age, colonization and persistence ability, we use two complementary model types: phenomenological linear models and the process‐based metapopulation model of Levins. Results Linear model analyses show that range filling increases with a species’ colonization and persistence ability but is not affected by species age. Moreover, colonization ability is a better predictor of range filling than its component variables (local abundance and dispersal ability). The phylogenetically independent interaction between colonization and persistence ability is significant (P < 0.05) for 97% of 180 alternative phylogenies. While the selected linear model explains 42% of the variance in arcsine transformed range filling, the Levins model performs more poorly. It overestimates range filling for realistic parameter values and produces unrealistic parameter estimates when fitted statistically. Main conclusions Colonization and local extinction seem to shape Proteaceae range dynamics on ecological rather than macroevolutionary time‐scales. Our results suggest that the positive abundance–range size relationship in this group is due primarily to the effect of abundance on colonization. In summary, this study contributes to a process‐based understanding of range dynamics and highlights the importance of colonization for the future survival of Fynbos Proteaceae.