Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds

Combining observational and experimental data to estimate environmental and species drivers of fungal metacommunity dynamics

Understanding the distribution and dynamics of species is central to ecology and important for managing biodiversity. The distributions of species in metacommunities are determined by many factors, including environmental conditions and interactions between species. Yet, it is difficult to quantify the effect of species interactions on metacommunity dynamics from observational data. We present an approach to estimate the importance of species interactions that combines data from two observational presence-absence inventories (providing colonization-extinction data) with data from species interaction experiments (providing informative prior distributions in the Bayesian framework). We further illustrate the approach on wood-decay fungi that interact within a downed log through competition for resources and space, and facilitate the succession of other species by decomposing the wood. Specifically, we estimated the relative importance of species interactions by examining how the presence of a species influenced the colonization and extinction probability of other species. Temporal data on fruit body occurrence of 12 species inventoried twice were jointly analyzed with experimental data from two laboratory experiments that aimed to estimate competitive interactions. Both environmental variables and species interactions affected colonization and extinction dynamics. Late-successional fungi had more colonization interactions with predecessor species than early-successional species. We identified several species interactions, and the presence of certain species changed the probability that later-successional species colonized by -81% to 512%. The presence of certain species increased the probability that other species went extinct from a log by 14%-61%. Including the informative priors from experimental data added two colonization interactions and one extinction interaction for which the observational field data was inconclusive. However, most species had no detectable interactions, either because they did not interact or because of low species occupancy, meaning data limitation. We show how temporal presence-absence data can be combined with experimental data to identify which species influence the colonization-extinction dynamics of others. Accounting for species interactions in metacommunity models, in addition to environmental drivers, is important because interactions can have cascading effects on other species.

Quantitative evaluation of the global impacts of human land modification on raptors

Raptors are threatened by anthropogenic land modifications, but targeted quantitative assessment of these impacts is lacking. We conducted the first global quantitative evaluation of the impacts of human-modified land on raptors. We used eBird data from 2001 to 2020 on 425 raptor species and occupancy models to assess the impacts of human-modified land on raptor distribution. The mean spatiotemporal correlations of human settlement, cropland, and pasture with raptor occupancy probability were -0.048 (SE 0.031), -0.134 (0.032), and -0.145 (0.032), respectively. The mean sensitivity of raptor occupancy probability to settlement, cropland, and pasture was -5.760 (2.266), -3.128 (1.540), and -2.402 (1.551), respectively. The occupancy probability of raptors with a large body mass was more negatively correlated with cropland (phylogenetic generalized least squares regressions: slope = -0.052 [SE 0.022], t = -2.335, df = 1, 407, p = 0.020, λ = 0.006) and more positively correlated with pasture (slope = 0.047 [0.022], t = 2.118, df = 1, 407, p = 0.035, λ = 0.013). The occupancy probability of raptors with a more extensive range size was more positively correlated with cropland (slope = 0.002 [0.004], t = 0.399, df = 1, 407, p < 0.001, λ = 0.000). Raptors that prefer open habitats were more positively correlated with cropland (analysis of variance: F = 3.424, df = 2, p = 0.034, λ = 0.000) and pasture (F = 6.577, df = 2, p = 0.002, λ = 0.000). In Africa and South America, where raptor species are most abundant, raptor occupancy probability decreased over 20 years, most likely due to habitat fragmentation associated with human land modification. Although raptors with different ecological characteristics had different responses to human land modification, the impacts of settlement, cropland, and pasture on mean raptor occupancy probability were negative, regardless of space and time.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Spatial distribution dynamics for Epimedium brevicornum Maxim. from 1970 to 2020

At different time scales, a species will experience diverse distribution changes. For Epimedium brevicornum Maxim, the phenomenon is obvious, but the understanding of the spatial dynamics of E. brevicornum under distinct time scales is poor. In this study, we modeled the potential distribution for E. brevicornum for five time scales, 1970-1979, 1980-1989, 1990-1999, 2000-2009, and 2010-2019, with different occurrence data, and the Kuenm package was used to optimize the parameter combination. Then, SDM tools and a Venn diagram were utilized to simulate the changes in highly suitable areas and spatial dynamics, respectively. Comprehensive results show that temperature seasonality (BIO4, 37.54%) has the greatest effect on the distribution of E. brevicornum, followed by minimum temperature (TMIN, 21.42%). The areas of distribution for E. brevicornum are 35.06 × 105 km2, 25.7 × 105 km2, 67.64 × 105 km2, 27.29 × 105 km2, and 9.87× 105 km2, which are mainly concentrated in Gansu, Shaanxi, Shanxi, and Henan, respectively. In addition, the largest regions for expansion, stability, and contraction under various time scales are 5.6 × 105 km2, 3.54 × 105 km2, and 3.47 × 105 km2, respectively. These changes indicate that approximately 7.96% of the regions are highly stable, and three critical counties, Wanyuan, Chenggu, and Hechuan, and Xixiang, have become significant areas for migration. Overall, our results indicate that there are different spatial distribution patterns and dynamics for E. brevicornum for different time scales. Given these results, this study also proposes comprehensive strategies for the conservation and management of E. brevicornum, which will further improve the current resource utilization status.

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

Local colonisations and extinctions of European birds are poorly explained by changes in climate suitability

Climate change has been associated with both latitudinal and elevational shifts in species' ranges. The extent, however, to which climate change has driven recent range shifts alongside other putative drivers remains uncertain. Here, we use the changing distributions of 378 European breeding bird species over 30 years to explore the putative drivers of recent range dynamics, considering the effects of climate, land cover, other environmental variables, and species' traits on the probability of local colonisation and extinction. On average, species shifted their ranges by 2.4 km/year. These shifts, however, were significantly different from expectations due to changing climate and land cover. We found that local colonisation and extinction events were influenced primarily by initial climate conditions and by species' range traits. By contrast, changes in climate suitability over the period were less important. This highlights the limitations of using only climate and land cover when projecting future changes in species' ranges and emphasises the need for integrative, multi-predictor approaches for more robust forecasting.

Demography-environment relationships improve mechanistic understanding of range dynamics under climate change

Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography-climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes-juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.

Occupancy winners in tropical protected forests: a pantropical analysis

The structure of forest mammal communities appears surprisingly consistent across the continental tropics, presumably due to convergent evolution in similar environments. Whether such consistency extends to mammal occupancy, despite variation in species characteristics and context, remains unclear. Here we ask whether we can predict occupancy patterns and, if so, whether these relationships are consistent across biogeographic regions. Specifically, we assessed how mammal feeding guild, body mass and ecological specialization relate to occupancy in protected forests across the tropics. We used standardized camera-trap data (1002 camera-trap locations and 2-10 years of data) and a hierarchical Bayesian occupancy model. We found that occupancy varied by regions, and certain species characteristics explained much of this variation. Herbivores consistently had the highest occupancy. However, only in the Neotropics did we detect a significant effect of body mass on occupancy: large mammals had lowest occupancy. Importantly, habitat specialists generally had higher occupancy than generalists, though this was reversed in the Indo-Malayan sites. We conclude that habitat specialization is key for understanding variation in mammal occupancy across regions, and that habitat specialists often benefit more from protected areas, than do generalists. The contrasting examples seen in the Indo-Malayan region probably reflect distinct anthropogenic pressures.

From design to analysis: A roadmap for predicting distributions of rare species

Rare species are challenging to study, in part because rarity can take many forms. Jeliazkov et al. guide us through the multiple decisions to be made-from sampling designs to field methods and analytical, integrated models. Improved monitoring methods are needed to improve our understanding of rare species importance for ecosystem structure and functions. This is a commentary on Jeliazkov et al., 2022, https://doi.org/10.1111/gcb.16114.

Regional entrepreneurial ecosystems: learning from forest ecosystems

Despite the emerging body of literature on entrepreneurial ecosystems (EEs), theoretical development is still in its infancy. In this article, we explicitly draw upon the analogy of forest ecosystems (FEs) with an EE to extrapolate the regional entrepreneurial ecosystem (REE) as an alternate conceptual framework. The REE considers a region's socioeconomic activity and the stability of its performance as a whole, influenced by partitioned interests of economics, social arrangements, physical environment, knowledge and the technology that each contributes to the community's industry and economic order. We contend that it is when an EE is defined by a regional dimension that it is analogous to the study of forests. In this REE analysis, neither the entrepreneur nor their firm are the unit of analysis, but it is the change and stability of the regional socioeconomic ecosystem itself that becomes the priority. Scholars, interested in the effects of entrepreneurship, can learn from ecological studies to more fully grasp the interplay between compositional, structural, and functional elements and specifically how entrepreneurs account for change dynamics.