Human and climatic change impact modelling on the habitat suitability for the chimpanzee (Pan troglodytes ellioti) – Case study: The proposed Mount Cameroon National Park

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

Climate and land-use changes effects on the distribution of a regional endemism: Melanophryniscus sanmartini (Amphibia, Bufonidae)

ABSTRACT Amphibians are the most threatened vertebrate group according to the IUCN. Land-use and land cover change (LULCC) and climate change (CC) are two of the main factors related to declining amphibian populations. Given the vulnerability of threatened and rare species, the study of their response to these impacts is a conservation priority. The aim of this work was to analyze the combined impact of LULCC and CC on the regionally endemic species Melanophryniscus sanmartini Klappenbach, 1968. This species is currently categorized as near threatened by the IUCN, and previous studies suggest negative effects of projected changes in climate. Using maximum entropy methods we modeled the effects of CC on the current and mid-century distribution of M. sanmartini under two IPCC scenarios - A2 (severe) and B2 (moderate). The effects of LULCC were studied by superimposing the potential distribution with current land use, while future distribution models were evaluated under the scenario of maximum expansion of soybean and afforestation in Uruguay. The results suggest that M. sanmartini is distributed in eastern Uruguay and the south of Brazil, mainly related to hilly and grasslands systems. Currently more than 10% of this species' distribution is superimposed by agricultural crops and exotic forest plantations. Contrasting with a recent modelling study our models suggest an expansion of the distribution of M. sanmartini by mid-century under both climate scenarios. However, despite the rise in climatically suitable areas for the species in the future, LULCC projections indicate that the proportion of modified habitats will occupy up to 25% of the distribution of M. sanmartini. Future change in climate conditions could represent an opportunity for M. sanmartini, but management measures are needed to mitigate the effects of habitat modification in order to ensure its survival and allow the eventual expansion of its distribution.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking approaches

Aim This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S-SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient.Location Two study areas in the Alps of Switzerland.Methods We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways - summing binary predictions, summing random draws of binomial trials and summing predicted probabilities - to obtain a final species count.Results The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S-SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump-shaped pattern of SR observed along the elevational gradient. The S-SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S-SDM approaches the summed binomial trials based on predicted probabilities and summed predicted probabilities - do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S-SDM approaches fail to appropriately reproduce the observed hump-shaped patterns of SR along the elevational gradient.Main conclusions Macroecological approach and S-SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S-SDM by MEM predictions.

Anthropogenic influence on the distribution, abundance and diversity of sandfly species (Diptera: Phlebotominae: Psychodidae), vectors of cutaneous leishmaniasis in Panama

In Panama, species of the genus Lutzomyia are vectors of American cutaneous leishmaniasis (ACL). There is no recent ecological information that may be used to develop tools for the control of this disease. Thus, the goal of this study was to determine the composition, distribution and diversity of Lutzomyia species that serve as vectors of ACL. Sandfly sampling was conducted in forests, fragmented forests and rural environments, in locations with records of ACL. Lutzomyia gomezi, Lutzomyia panamensis and Lutzomyia trapidoi were the most widely distributed and prevalent species. Analysis of each sampling point showed that the species abundance and diversity were greatest at points located in the fragmented forest landscape. However, when the samples were grouped according to the landscape characteristics of the locations, there was a greater diversity of species in the rural environment locations. The Kruskal Wallis analysis of species abundance found that Lu. gomezi and Lu. trapidoi were associated with fragmented environments, while Lu. panamensis, Lutzomyia olmeca bicolor and Lutzomyia ylephiletor were associated with forested environments. Therefore, we suggest that human activity influences the distribution, composition and diversity of the vector species responsible for leishmaniasis in Panama.

Adapting global conservation strategies to climate change at the European scale: the otter as a flagship species

Climate change has created the need for new strategies in conservation planning that account for the dynamics of factors threatening endangered species. Here we assessed climate change threat to the European otter, a flagship species for freshwater ecosystems, considering how current conservation areas will perform in preserving the species in a climatically changed future. We used an ensemble forecasting approach considering six modelling techniques applied to eleven subsets of otter occurrences across Europe. We performed a pseudo-independent and an internal evaluation of predictions. Future projections of species distribution were made considering the A2 and B2 scenarios for 2080 across three climate models: CCCMA-CGCM2, CSIRO-MK2 and HCCPR HAD-CM3. The current and the predicted otter distributions were used to identify priority areas for the conservation of the species, and overlapped to existing network of protected areas. Our projections show that climate change may profoundly reshuffle the otter's potential distribution in Europe, with important differences between the two scenarios we considered. Overall, the priority areas for conservation of the otter in Europe appear to be unevenly covered by the existing network of protected areas, with the current conservation efforts being insufficient in most cases. For a better conservation, the existing protected areas should be integrated within a more general conservation and management strategy incorporating climate change projections. Due to the important role that the otter plays for freshwater habitats, our study further highlights the potential sensitivity of freshwater habitats in Europe to climate change.

Combining palaeodistribution modelling and phylogeographical approaches for identifying glacial refugia in Alpine Primula

Aim We investigated the late Quaternary history of two closely related and partly sympatric species of Primula from the south-western European Alps, P. latifolia Lapeyr. and P. marginata Curtis, by combining phylogeographical and palaeodistribution modelling approaches. In particular, we were interested in whether the two approaches were congruent and identified the same glacial refugia. Location South-western European Alps. Methods For the phylogeographical analysis we included 353 individuals from 28 populations of P. marginata and 172 individuals from 15 populations of P. latifolia and used amplified fragment length polymorphisms (AFLPs). For palaeodistribution modelling, species distribution models (SDMs) were based on extant species occurrences and then projected to climate models (CCSM, MIROC) of the Last Glacial Maximum (LGM), approximately 21 ka. Results The locations of the modelled LGM refugia were confirmed by various indices of genetic variation. The refugia of the two species were largely geographically isolated, overlapping only 6% to 11% of the species' total LGM distribution. This overlap decreased when the position of the glacial ice sheet and the differential elevational and edaphic distributions of the two species were considered. Main conclusions The combination of phylogeography and palaeodistribution modelling proved useful in locating putative glacial refugia of two alpine species of Primula. The phylogeographical data allowed us to identify those parts of the modelled LGM refugial area that were likely source areas for recolonization. The use of SDMs predicted LGM refugial areas substantially larger and geographically more divergent than could have been predicted by phylogeographical data alone

Predicting global change impacts on plant species' distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species' range shifts, changes in phenology and species' extinctions, accurate projections of species' responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species' responses to future environmental changes. There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species' distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the "trailing edge" of shifting populations, species' interactions and the interaction between the effects of climate and land-use. In this review, we propose two main avenues to progress the understanding and prediction of the different processes A occurring on the leading and trailing edge of the species' distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species' migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species' distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world. (c) 2007 Rubel Foundation, ETH Zurich. Published by Elsevier GrnbH. All rights reserved.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

MIGCLIM: Predicting plant distribution and dispersal in a changing climate

Many studies have forecasted the possible impact of climate change on plant distribution using models based on ecological niche theory. In their basic implementation, niche-based models do not constrain predictions by dispersal limitations. Hence, most niche-based modelling studies published so far have assumed dispersal to be either unlimited or null. However, depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under- or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of "potentially suitable" and "potentially colonisable" habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed MIGCLIM, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. MIGCLIM implements various parameters, such as dispersal distance, increase in reproductive potential over time, barriers to dispersal or long distance dispersal. Several simulations were run for two virtual species in a study area of the western Swiss Alps, by varying dispersal distance and other parameters. Each simulation covered the hundred-year period 2001-2100 and three different IPCC-based temperature warming scenarios were considered. Our results indicate that: (i) using realistic parameter values, the future potential distributions generated using MIGCLIM can differ significantly (up to more than 95% decrease in colonized surface) from those that ignore dispersal; (ii) this divergence increases both with increasing climate warming and over longer time periods; (iii) the uncertainty associated with the warming scenario can be nearly as large as the one related to dispersal parameters; (iv) accounting for dispersal, even roughly, can importantly reduce uncertainty in projections.

Do habitat suitability models reliably predict the recovery areas of threatened species?

1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species' potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated.2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models' capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river.3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river's suitability and badly describing the otter's niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models' fit to the recolonization observations.4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors.5. Synthesis and applications. Conservation plans depend on reliable models of the species' suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.

A new spin on a compositionalist predictive modelling framework for conservation planning: a tropical case study in Ecuador

Knowledge about spatial biodiversity patterns is a basic criterion for reserve network design. Although herbarium collections hold large quantities of information, the data are often scattered and cannot supply complete spatial coverage. Alternatively, herbarium data can be used to fit species distribution models and their predictions can be used to provide complete spatial coverage and derive species richness maps. Here, we build on previous effort to propose an improved compositionalist framework for using species distribution models to better inform conservation management. We illustrate the approach with models fitted with six different methods and combined using an ensemble approach for 408 plant species in a tropical and megadiverse country (Ecuador). As a complementary view to the traditional richness hotspots methodology, consisting of a simple stacking of species distribution maps, the compositionalist modelling approach used here combines separate predictions for different pools of species to identify areas of alternative suitability for conservation. Our results show that the compositionalist approach better captures the established protected areas than the traditional richness hotspots strategies and allows the identification of areas in Ecuador that would optimally complement the current protection network. Further studies should aim at refining the approach with more groups and additional species information.

Making better biogeographical predictions of species' distributions

1. Biogeographical models of species' distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2. Most models still largely ignore or inappropriately take into account important features of species' distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species' predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4. Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species' distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species' distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.

Linking landscape- and population ecology for large population management modelling: the case of Ibex (capra ibex) in Switzerland

La biologie de la conservation est communément associée à la protection de petites populations menacées d?extinction. Pourtant, il peut également être nécessaire de soumettre à gestion des populations surabondantes ou susceptibles d?une trop grande expansion, dans le but de prévenir les effets néfastes de la surpopulation. Du fait des différences tant quantitatives que qualitatives entre protection des petites populations et contrôle des grandes, il est nécessaire de disposer de modèles et de méthodes distinctes. L?objectif de ce travail a été de développer des modèles prédictifs de la dynamique des grandes populations, ainsi que des logiciels permettant de calculer les paramètres de ces modèles et de tester des scénarios de gestion. Le cas du Bouquetin des Alpes (Capra ibex ibex) - en forte expansion en Suisse depuis sa réintroduction au début du XXème siècle - servit d?exemple. Cette tâche fut accomplie en trois étapes : En premier lieu, un modèle de dynamique locale, spécifique au Bouquetin, fut développé : le modèle sous-jacent - structuré en classes d?âge et de sexe - est basé sur une matrice de Leslie à laquelle ont été ajoutées la densité-dépendance, la stochasticité environnementale et la chasse de régulation. Ce modèle fut implémenté dans un logiciel d?aide à la gestion - nommé SIM-Ibex - permettant la maintenance de données de recensements, l?estimation automatisée des paramètres, ainsi que l?ajustement et la simulation de stratégies de régulation. Mais la dynamique d?une population est influencée non seulement par des facteurs démographiques, mais aussi par la dispersion et la colonisation de nouveaux espaces. Il est donc nécessaire de pouvoir modéliser tant la qualité de l?habitat que les obstacles à la dispersion. Une collection de logiciels - nommée Biomapper - fut donc développée. Son module central est basé sur l?Analyse Factorielle de la Niche Ecologique (ENFA) dont le principe est de calculer des facteurs de marginalité et de spécialisation de la niche écologique à partir de prédicteurs environnementaux et de données d?observation de l?espèce. Tous les modules de Biomapper sont liés aux Systèmes d?Information Géographiques (SIG) ; ils couvrent toutes les opérations d?importation des données, préparation des prédicteurs, ENFA et calcul de la carte de qualité d?habitat, validation et traitement des résultats ; un module permet également de cartographier les barrières et les corridors de dispersion. Le domaine d?application de l?ENFA fut exploré par le biais d?une distribution d?espèce virtuelle. La comparaison à une méthode couramment utilisée pour construire des cartes de qualité d?habitat, le Modèle Linéaire Généralisé (GLM), montra qu?elle était particulièrement adaptée pour les espèces cryptiques ou en cours d?expansion. Les informations sur la démographie et le paysage furent finalement fusionnées en un modèle global. Une approche basée sur un automate cellulaire fut choisie, tant pour satisfaire aux contraintes du réalisme de la modélisation du paysage qu?à celles imposées par les grandes populations : la zone d?étude est modélisée par un pavage de cellules hexagonales, chacune caractérisée par des propriétés - une capacité de soutien et six taux d?imperméabilité quantifiant les échanges entre cellules adjacentes - et une variable, la densité de la population. Cette dernière varie en fonction de la reproduction et de la survie locale, ainsi que de la dispersion, sous l?influence de la densité-dépendance et de la stochasticité. Un logiciel - nommé HexaSpace - fut développé pour accomplir deux fonctions : 1° Calibrer l?automate sur la base de modèles de dynamique (par ex. calculés par SIM-Ibex) et d?une carte de qualité d?habitat (par ex. calculée par Biomapper). 2° Faire tourner des simulations. Il permet d?étudier l?expansion d?une espèce envahisseuse dans un paysage complexe composé de zones de qualité diverses et comportant des obstacles à la dispersion. Ce modèle fut appliqué à l?histoire de la réintroduction du Bouquetin dans les Alpes bernoises (Suisse). SIM-Ibex est actuellement utilisé par les gestionnaires de la faune et par les inspecteurs du gouvernement pour préparer et contrôler les plans de tir. Biomapper a été appliqué à plusieurs espèces (tant végétales qu?animales) à travers le Monde. De même, même si HexaSpace fut initialement conçu pour des espèces animales terrestres, il pourrait aisément être étndu à la propagation de plantes ou à la dispersion d?animaux volants. Ces logiciels étant conçus pour, à partir de données brutes, construire un modèle réaliste complexe, et du fait qu?ils sont dotés d?une interface d?utilisation intuitive, ils sont susceptibles de nombreuses applications en biologie de la conservation. En outre, ces approches peuvent également s?appliquer à des questions théoriques dans les domaines de l?écologie des populations et du paysage.<br/><br/>Conservation biology is commonly associated to small and endangered population protection. Nevertheless, large or potentially large populations may also need human management to prevent negative effects of overpopulation. As there are both qualitative and quantitative differences between small population protection and large population controlling, distinct methods and models are needed. The aim of this work was to develop theoretical models to predict large population dynamics, as well as computer tools to assess the parameters of these models and to test management scenarios. The alpine Ibex (Capra ibex ibex) - which experienced a spectacular increase since its reintroduction in Switzerland at the beginning of the 20th century - was used as paradigm species. This task was achieved in three steps: A local population dynamics model was first developed specifically for Ibex: the underlying age- and sex-structured model is based on a Leslie matrix approach with addition of density-dependence, environmental stochasticity and culling. This model was implemented into a management-support software - named SIM-Ibex - allowing census data maintenance, parameter automated assessment and culling strategies tuning and simulating. However population dynamics is driven not only by demographic factors, but also by dispersal and colonisation of new areas. Habitat suitability and obstacles modelling had therefore to be addressed. Thus, a software package - named Biomapper - was developed. Its central module is based on the Ecological Niche Factor Analysis (ENFA) whose principle is to compute niche marginality and specialisation factors from a set of environmental predictors and species presence data. All Biomapper modules are linked to Geographic Information Systems (GIS); they cover all operations of data importation, predictor preparation, ENFA and habitat suitability map computation, results validation and further processing; a module also allows mapping of dispersal barriers and corridors. ENFA application domain was then explored by means of a simulated species distribution. It was compared to a common habitat suitability assessing method, the Generalised Linear Model (GLM), and was proven better suited for spreading or cryptic species. Demography and landscape informations were finally merged into a global model. To cope with landscape realism and technical constraints of large population modelling, a cellular automaton approach was chosen: the study area is modelled by a lattice of hexagonal cells, each one characterised by a few fixed properties - a carrying capacity and six impermeability rates quantifying exchanges between adjacent cells - and one variable, population density. The later varies according to local reproduction/survival and dispersal dynamics, modified by density-dependence and stochasticity. A software - named HexaSpace - was developed, which achieves two functions: 1° Calibrating the automaton on the base of local population dynamics models (e.g., computed by SIM-Ibex) and a habitat suitability map (e.g. computed by Biomapper). 2° Running simulations. It allows studying the spreading of an invading species across a complex landscape made of variously suitable areas and dispersal barriers. This model was applied to the history of Ibex reintroduction in Bernese Alps (Switzerland). SIM-Ibex is now used by governmental wildlife managers to prepare and verify culling plans. Biomapper has been applied to several species (both plants and animals) all around the World. In the same way, whilst HexaSpace was originally designed for terrestrial animal species, it could be easily extended to model plant propagation or flying animals dispersal. As these softwares were designed to proceed from low-level data to build a complex realistic model and as they benefit from an intuitive user-interface, they may have many conservation applications. Moreover, theoretical questions in the fields of population and landscape ecology might also be addressed by these approaches.