Species interactions weakly modify climate‐induced tree co‐occurrence patterns

Aims

Species distributions are hypothesized to be underlain by a complex association of processes that span multiple spatial scales including biotic interactions, dispersal limitation, fine‐scale resource gradients and climate. Species disequilibrium with climate may reflect the effects of non‐climatic processes on species distributions, yet distribution models have rarely directly considered non‐climatic processes. Here, we use a Joint Species Distribution Model (JSDM) to investigate the influence of non‐climatic factors on species co‐occurrence patterns and to directly quantify the relative influences of climate and alternative processes that may generate correlated responses in species distributions, such as species interactions, on tree co‐occurrence patterns.

Location

US Rocky Mountains.

Methods

We apply a Bayesian JSDM to simultaneously model the co‐occurrence patterns of ten dominant tree species across the Rocky Mountains, and evaluate climatic and residual correlations from the fitted model to determine the relative contribution of each component to observed co‐occurrence patterns. We also evaluate predictions generated from the fitted model relative to a single‐species modelling approach.

Results

For most species, correlation due to climate covariates exceeded residual correlation, indicating an overriding influence of broad‐scale climate on co‐occurrence patterns. Accounting for covariance among species did not significantly improve predictions relative to a single‐species approach, providing limited evidence for a strong independent influence of species interactions on distribution patterns.

Conclusions

Overall, our findings indicate that climate is an important driver of regional biodiversity patterns and that interactions between dominant tree species contribute little to explain species co‐occurrence patterns among Rocky Mountain trees.     

Modelling climate‐change‐induced nonlinear thresholds in cephalopod population dynamics

A significant global challenge lies in our current inability to anticipate, and therefore prepare for, critical ecological thresholds (i.e. tipping points in ecosystems). This deficit stems largely from an inadequate understanding of the many complex interactions between species and the environment at the ecosystem level, and the paucity of mechanistic models relating environment to population dynamics at the species level. In marine ecosystems, abundant, short‐lived and fast‐growing species such as anchovies or squids, consistently function as ‘keystone’ groups whose population dynamics affect entire ecosystems. Increasing exploitation coupled with climate change impacts has the potential to affect these ecological groups and consequently, the entire marine ecosystem. There are currently very few models that predict the impact of climate change on these keystone groups. Here we use a combination of individual‐based bioenergetics and stage‐structured population models to characterize the fundamental capacity of cephalopods to respond to climate change. We demonstrate the potential for, and mechanisms behind, two unfavourable climate‐change‐induced thresholds in future population dynamics. Although one threshold was the direct consequence of a decrease in incubation time caused by ocean warming, the other threshold was linked to survivorship, implying the possibility of management through a modification of fishing mortality. Additional substantive changes in phenology were also predicted, with a possible loss in population resilience. Our results demonstrate the feasibility of predicting complex nonlinear dynamics with a reasonably simplistic mechanistic model, and highlight the necessity of developing such approaches for other species if attempts to moderate the impact of climate change on natural resources are to be effective.     

The Mediterranean Sea as a ‘cul‐de‐sac’ for endemic fishes facing climate change

The Mediterranean Sea is a hotspot of biodiversity, and climate warming is expected to have a significant influence on its endemic fish species. However, no previous studies have predicted whether fish species will experience geographic range extensions or contractions as a consequence of warming. Here, we projected the potential future climatic niches of 75 Mediterranean Sea endemic fish species based on a global warming scenario implemented with the Mediterranean model OPAMED8 and a multimodel inference, which included uncertainty. By 2070–2099, the average surface temperature of the Mediterranean Sea was projected to warm by 3.1 °C. Projections for 2041–2060 are that 25 species would qualify for the International Union for the Conservation of Nature and Natural Resources (IUCN) Red List, and six species would become extinct. By 2070–2099, 45 species were expected to qualify for the IUCN Red List whereas 14 were expected to become extinct. By the middle of the 21st century, the coldest areas of the Mediterranean Sea (Adriatic Sea and Gulf of Lion) would act as a refuge for cold‐water species, but by the end of the century, those areas were projected to become a ‘cul‐de‐sac’ that would drive those species towards extinction. In addition, the range size of endemic species was projected to undergo extensive fragmentation, which is a potentially aggravating factor. Since a majority of endemic fishes are specialists, regarding substratum and diet, we may expect a reduced ability to track projected climatic niches. As a whole, 25% of the Mediterranean Sea continental shelf was predicted to experience a total modification of endemic species assemblages by the end of the 21st century. This expected turnover rate could be mitigated by marine protected areas or accelerated by fishing pressure or competition from exotic fishes. It remains a challenge to predict how these assemblage modifications might affect ecosystem function.     

Genetic trials improve the transfer of Douglas‐fir distribution models across continents

Climate change is likely to result in novel conditions with no analogy to current climate. Therefore, the application of species distribution models (SDMs) based on the correlation between observed species’ occurrence and their environment is questionable and calls for a better understanding of the traits that determine species occurrence. Here, we compared two intraspecific, trait‐based SDMs with occurrence‐based SDMs, all developed from European data, and analyzed their transferability to the native range of Douglas‐fir in North America. With data from 50 provenance trials of Douglas‐fir in central Europe multivariate universal response functions (URFs) were developed for two functional traits (dominant tree height and basal area) which are good indicators of growth and vitality under given environmental conditions. These trials included 290 North American provenances of Douglas‐fir. The URFs combine genetic effects i.e. the climate of provenance origin and environmental effects, i.e. the climate of planting locations into an integrated model to predict the respective functional trait from climate data. The URFs were applied as SDMs (URF‐SDMs) by converting growth performances into occurrence. For comparison, an ensemble occurrence‐based SDM was developed and block cross validated with the BIOMOD2 modeling platform utilizing the observed occurrence of Douglas‐fir in Europe. The two trait based SDMs and the occurrence‐based SDM, all calibrated with data from Europe, were applied to predict the known distribution of Douglas‐fir in its introduced and native range in Europe and North America. Both models performed well within their calibration range in Europe, but model transfer to its native range in North America was superior in case of the URF‐SDMs showing similar predictive power as SDMs developed in North America itself. The high transferability of the URF‐SDMs is a testimony of their applicability under novel climatic conditions highlighting the role of intraspecific trait variation for adaptation planning in climate change.     

Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites

Most high‐performing species distribution modelling techniques require both presences, and either absences or pseudo‐absences or background points. In this paper, we explore the effect of sample size, towards developing improved strategies for modelling. We generated 1800 virtual species with three levels of prevalence using ten modelling techniques, while varying the number of training presences (NTP) and the number of random points (NRP representing pseudo‐absences or background sites). For five of the ten modelling techniques we built two versions of models: one with an equal total weight (ETW) setting where the total weight for pseudo‐absence is equivalent to the total weight for presence, and another with an unequal total weight (UTW) setting where the total weight for pseudo‐absence is not required to be equal to the total weight for presence. We compared two strategies for NRP: a small multiplier strategy (i.e. setting NRP at a few times as large as NTP), and a large number strategy (i.e. using numerous random points). We produced ensemble models (by averaging the predictions from 30 models built with the same set of training presences and different sets of random points in equivalent numbers) for three NTP magnitudes and two NRP strategies. We found that model accuracy altered as NRP increased with four distinct patterns of performance: increasing, decreasing, arch‐shaped and horizontal. In most cases ETW improved model performance. Ensemble models had higher accuracy than the corresponding single models, and this improvement was pronounced when NTP was low. We conclude that a large NRP is not always an appropriate strategy. The best choice for NRP will depend on the modelling techniques used, species prevalence and NTP. We recommend building ensemble models instead of single models, using the small multiplier strategy for NRP with ETW, especially when only a small number of species presence records are available.     

A socio‐ecological model for predicting impacts of land‐use and climate change on regional plant diversity in the Austrian Alps

Climate and land‐use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent‐based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine‐grained land‐use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land‐use patterns resulting from their response to three scenarios of changing socio‐economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land‐use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land‐use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land‐use changes because alternative future socio‐economic backgrounds have only modest impact on land‐use decisions in the model region. However, relative effects of climate and land‐use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi‐natural habitat. We conclude that agent‐based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.     

Using space‐for‐time substitution and time sequence approaches in invasion ecology

1. Invasion biologists use two main approaches to evaluate the effects of non‐native species (NNS) on diversity of native species (DNS), namely space‐for‐time and time approaches. These approaches have pitfalls related to lack of controls: the former lacks pre‐invasion data, while the latter often lacks data from non‐invaded sites. 2. We propose a framework that combines space‐for‐time and time approaches and which should result in more focused mechanistic hypotheses and experiments to test the causes of invasibility and the effects of NNS on DNS. We illustrate the usefulness of our framework using two case studies: one with the submersed macrophyte, Hydrilla verticillata, in reservoir and the other with the fish, Geophagus proximus, in a large river–floodplain system. 3. Hydrilla verticillata invaded sites with DNS similar to that found in non‐invaded sites, indicating that biotic and/or abiotic factors did not influence invasion success; however, DNS increased over time in invaded sites compared with non‐invaded sites, suggesting that H. verticillata facilitated natives. In contrast, G. proximus invaded sites with higher DNS than non‐invaded sites, suggesting that biotic and/or abiotic factors favouring natives were important for invasion success, but DNS increased in invaded and non‐invaded sites over time, indicating that an independent factor contributed to DNS increases. 4. Conclusions from both studies would have been inaccurate or incomplete if the space‐for‐time and time approaches had not been used in combination as proposed in our framework.     

Species‐time‐area and phylogenetic‐time‐area relationships in tropical tree communities

The species‐area relationship (SAR) has proven to be one of the few strong generalities in ecology. The temporal analog of the SAR, the species‐time relationship (STR), has received considerably less attention. Recent work primarily from the temperate zone has aimed to merge the SAR and the STR into a synthetic and unified species‐time‐area relationship (STAR) as originally envisioned by Preston (1960). Here we test this framework using two tropical tree communities and extend it by deriving a phylogenetic‐time‐area relationship (PTAR). The work finds some support for Preston's prediction that diversity‐time relationships, both species and phylogenetic, are sensitive to the spatial scale of the sampling. Contrary to the Preston's predictions we find a decoupling of diversity‐area and diversity‐time relationships in both forests as the time period used to quantify the diversity‐area relationship changes. In particular, diversity‐area and diversity‐time relationships are positively correlated using the initial census to quantify the diversity‐area relationship, but weakly or even negatively correlated when using the most recent census. Thus, diversity‐area relationships could forecast the temporal accumulation of biodiversity of the forests, but they failed to “back‐cast” the temporal accumulation of biodiversity suggesting a decoupling of space and time.     

Flow‐mediated predator–prey dynamics influence fish populations in a tropical river

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Light matters: testing the “Light Environment Hypothesis” under intra‐ and interspecific contexts

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