Combining Observational Data and Language for Species Range Estimation
Authors: Max Hamilton
Christian Lange
Elijah Cole
Alexander Shepard
Samuel Heinrich
Oisin Mac Aodha
Grant Van Horn
Subhransu Maji
Venue: NeurIPS 2024 poster (2024)
Keywords: Species range estimation
zero-shot learning
few-shot learning
implicit networks
Primary area: machine_learning_for_physical_sciences
TL;DR: TL;DR: We developed a method for creating species range maps by integrating citizen science data with Wikipedia text, enabling accurate zero-shot and few-shot range estimation.

Abstract: Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management. However, traditional SRMs rely on the availability of environmental covariates and high-quality observational data, both of which can be challenging to obtain due to geographic inaccessibility and resource constraints. We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia, covering habitat preferences and range descriptions for tens of thousands of species. Our framework maps location, species, and text descriptions into a common space, facilitating the learning of rich spatial covariates at a global scale and enabling zero-shot range estimation from textual descriptions. Evaluated on held-out species, our zero-shot SRMs significantly outperform baselines and match the performance of SRMs obtained using tens of observations. Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data. We present extensive quantitative and qualitative analyses of the learned representations in the context of range estimation and other spatial tasks, demonstrating the effectiveness of our approach.

Combining Observational Data and Language for Species Range Estimation
Max Hamilton'
Christian Lange*
Elijah Cole
Alexander Shepard'
Samuel Heinrich
Oisin Mac Aodha²
Grant Van Horn'
Subhransu Maji'
UMMss Amherst
University of Edinburgh
Altos Labs
*iNaturalist
*Cornell University
Abstract
Species range maps (SRMs) are essential tools for research and policy-making ecology, conservation, and environmental management. However, traditional SRMs rely on the availability of environmental covariates and high-quality species location observation data, both of which can be challenging obtain due geographic inaccessibility and resource constraints. We propose novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia, covering habitat preferences and range descriptions for tens of thousands of species. Our framework maps locations, species, and text descriptions into common space, facilitating the learning of rich spatial covariates global scale and enabling zero-shot range estimation from textual descriptions. Evaluated held-out species, zero-shot SRMs significantly outperform baselines and match the performance of SRMs obtained using tens of observations. Our approach also acts strong prior when combined with observational data, resulting more accurate range estimation with less data. We present extensive quantitative and qualitative analyses of the learned representations in the context of range estimation and other spatial tasks, demonstrating the effectiveness of our approach.
Introduction
Collecting sufficient voint-based observations species in the wild allows us to infer species range maps (SRMs), which describe the spatial extent of where species is likely to occur. These maps
invaluable, enhancing our understanding natural history and informing land use and conservation
decisions. Large-scale citizen science projects like iNaturalist 2 and eBird (30 have recently accelerated SRM generation by systematically consolidating millions of observations across tens of thousands of species. By combining these extensive databases with environmental covariates, produce accurate SRMs. However, there remains a 'long tail' of species with few observations, and current methods fall short of producing reliable range maps in such low-data settings.
We propose learning SRMs by combining citizen science observations with text descriptions of species from Wikipedia (see Figure I. These text descriptions describe wide array of properties including habitat preferences, range estimates, and visual attributes. Our framework Iearns to map location embeddings over the Earth's 's surface and text embeddings from Wikipedia 4 articles into common space based on the species observations (see Figure 2 This provides a way to ground the information in text describing observations. As shown Figure [ our resulting model allows us to estimate SRMs based on text descriptions that might be known ecologist, such habitat preferences, even when location observations are available.
of thousands of species to spatial locations based millions of
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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"Fairly common in Andean cloud forest from Colombia Peru, typically from around 800-1800m"
LE-SINR
Description of Species' Range
Predicted Range
Figure I:
Our LE-SINR model takes as input free-form text describing aspects of species' preferred habitat or range and geospatially grounds it to generate a plausible range map for that species.
We evaluate text-driven approach for zero-shot estimation of SRMs for species from the IUCN
and eBird Status and Trends (S&T) [15] benchmarks from [0]] which were excluded from the training data. Our model easily outperforms baselines (Table I and competitive with SRMs estimated with ten observations for S&T species (Figure 3 Additionally, our model can be combined with observational data to achieve strong few-shot performance. Logistic regression models are regularized to be close to the species vector obtained from text descriptions, allowing us to match the performance of SRMs estimated using an order of magnitude more observations (Figure 3
While training is based on Wikipedia articles, it is unrealistic to expect biologist to provide such extensive information for novel species. Therefore, we evaluate model based on short summaries, often just a few sentences long, that describe various aspects of the species. We further organize the summaries based on descriptions of the range (e.g., where they appear) and habitat preferences. While a biologist might not know the full range of an unknown species, they may be familiar with some of the latter (e.g., a tree might be in a tropical forest, or a bird might prefer wetlands). However, training on both forms of data allows the location embeddings to capture wide range of geographic concepts embedded in language, such as countries, continents, climate regions, topology, and biomes, as revealed in our visualizations (Figures 4 and [). Even short text summaries provide significant boost estimation accuracy over baselines when there are zero or few observations available. Our code and data is publicly available at: https:://// //github. com umass aaaimaassssssssssa lin-si
Related work
Species distribution models (SDMs). SDMs are broad family of models, primarily from the ecological statistics literature, that are concerned with modeling and predicting different geospatial properties of species of interest [14 13). These spatially varying properties encompass quantities such as occurrence (i.e., the presence or absence of a species) through to abundance (i.e., a count of the number of individuals from given species present). By integrating occurrence predictions over the earth, we can generate the range of a species, which is defined as the geographic area in which species can be found during its lifetime. Existing works can be broadly categorized based on the completeness of the data they are trained on (e.g., presence-only vs. presence-absence data), the number of species they simultaneously model (e.g., single vs. joint methods), or how interpretable they are (e.g., machine learning vs. mechanistic models). For an introduction to SDMs, we point interested readers to the following survey (6).
Most relevant to our work is the growing number of deep SDM approaches. These methods explore the core task from representation learning perspective by training raw observation data, typically from multiple species simultaneously, to learn an encoding of geographic space that is more predictive of species presence. [IO] demonstrated the advantages of this approach by showing that model performance improves when trained on larger amounts of data, even if that data comes from disjoint species that do not appear in the evaluation set. Recent work has explored various challenges and design decisions related to training these models, addressing topics such as data imbalance [34] spatial biases [8), location encodings
(27]. the use of remote sensing data [T] B1, active learning [9] binarizing range maps [1]. and modeling species co-occurrence [9. However, the current literature has not extensively investigated the few-shot setting, where very limited or potentially no observation
data is available. There are estimated nine million species on Earth [23]. and given that only limited proportion of these have reliable range estimates, there a need for methods that can reliably estimate geospatial properties of interest from few observations.

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Input Text
Language Encoder
Species Tokens
Cynanthus auriceps
t
LLM
MLP
Actorshot
triin
distnotivee wholly emerald green jenwn is not really golden, more of goiden green) with very long. deeply focked blackish
g(t)
Ey
E
Pavees serubby weodland, edge, and flower banks, mainty in habitats
Input Location
Location Encoder
Predicted Range
o (
f(x))
dbier
Very small, fork talled humming bisd endemic Mexico
Inittade
X
MLP
latitude
Jongitude
longinade
Figure 2: LE-SINR learns to align location and text representations training time using presence- only observation data and habitat or range descriptions for a set of species. Optionally, we can also include a learnable species token Ey allowing range estimation for seen species y from the training set. The model is trained on millions of observations from iNaturalist and language data from Wikipedia articles across thousands of species. LE-SINR supports zero-shot range estimation based on text descriptions for novel species and can also be used as prior for few-shot range estimation.
Geospatial data and large language models (LLMs). High-capacity transformer-based archi- tectures (32). coupled with large web-sourced text training data, have largely contributed recent advances in LLMs. LLMs have been demonstrated to be effective across range of language- based reasoning tasks [35] 21 77) Inspired by this, recently there have been multiple attempts explore what, if any, geospatial information is encoded inside of these models. For example, (25) evaluated a pre-trained closed-source LLM (i.e., GPT-4 (5)) on range of geospatial tasks such as point based ones (e.g., location, distance, and elevation estimation) in addition to more complex path-based ones (e.g., geographic and outlines route planning) via carefully designed text prompts. They later extended this work to multimodal LLMs that can also take images as input [26] In both cases, they observed impressive capabilities, but also some notable limitations.
In [22 the authors used pre-trained LLMs to map geographic coordinates to continuous geospatial properties (e.g.. population, house value, etc.). They improved upon simple text prompts by engineer- ing a prompt which provides spatial context in terms of relative distance to nearby named locations to the query coordinate of interest. By fine-tuning the LLM they demonstrated that their approach works better than the more naive encoding. However, their approach is expensive to evaluate at inference time as requires a full forward pass of the LLM for every geographic coordinate of interest.
Most related to our work is LD-SDM (88) which also uses an LLM in conjunction with an SDM. Their goal is predict the spatial range of a set of species of interest using location observation data at training time. However, instead of simply learning a per-species latent embedding vector as in [10], they employ a frozen LLM map text string that describes the explicit taxonomic hierarchy (i.e., species, genus, family, etc.) of a species of interest to a latent embedding. Unlike us, their species text description is not very expressive (i.e., it has very specific hierarchical structure) and thus cannot generalize as effectively to distinct held-out species at evaluation time. Instead, in this work, we show that it is possible predict the range of a previously unseen species from free-form, highly unstructured, internet sourced text that describes its habitat and/or range preferences. Furthermore, once trained, we show that our approach is also able to efficiently and densely geospatially ground non-species related text. Unlike [22), our approach is computationally efficient at inference time, requiring only one LLM forward pass for the text query of species of interest, as opposed one forward pass for each location of interest, which can number in the millions depending on the spatial resolution at which the evaluation is performed.
Methods
3.1 Problem Setup
We focus on the problem of estimating SRMs across multiple species, indicating their presence or absence at each location on Earth. For given location x, our goal is to predict the probability of each species being present there. Our observation dataset composed of pairs {xx, v4) J.N h= where
3

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X; (lat, lon) is geographic location and Yi E [1.....,} is the observed species label. We use the dataset proposed in SINR [T0], consisting of 35.5 million observations covering 47,375 species observed prior to 2022 on the iNaturalist platform. Included species have at least 50 observations. Removing those that are included in the S&T and IUCN evaluation benchmarks results in 44,181 species, which forms our training set.
We also consider estimating SRMs from text-based descriptions in addition to observational data. To this end we curate a text dataset {t., y:)}, where t; is text description sourced from Wikipedia pertaining to species yt- For a particular species y/i and corresponding text description t,, we aim to predict whether y/ is present at some location x; given tç. It is important to emphasize that this prediction only depends on the text description t; and query location x, Thus, we are not constrained to only making range predictions for species that have been observed during training. This allows for zero-shot species range estimation, where we can generate range map of a species not in our observation dataset by utilizing a text description for it.
3.2
Text Data
We source our text descriptions from Wikipedia 4
For a particular species, we search for its Wikipedia article using its scientific name. We then extract the text and divide it into chunks by section. Every article starts with lead section, which typically provides an overview, followed by a varying number of body sections. These body sections can describe diverse range of attributes such as taxonomy, description, habitat, behavior, diet, etc. To improve data quality, we remove sections with "References," "Links," and "Bibliography" in their names. Since each species has multiple sections, at training time we randomly sample one section per iteration. Overall, our text dataset contains 227,484 sections from 37,889 species' articles. Note, not all species in our observation dataset have an associated text description.
3.3 Language Enhanced SINR (LE-SINR) Architecture
We utilize the Spatial Implicit Neural Representation (SINR) 10 framework for our approach, which models the probability of presence for species y at given location x as (x) EEy). where fo (x) is a location encoding, Ey is an embedding of species y and a is the sigmoid function. Our architecture, shown in Figure 2 is similarly composed of two branches: one for representing locations and one for representing species. Each branch outputs a 256-dimensions occurrence then estimated by the sigmoid of their dot product.
feature vector. The probability of
The location branch is a location encoder model, (x), with parameters 0, which takes a position embedding x (e.g.. a location denoted by latitude and longitude) as input. The species branch is composed of two models, allowing us to generate species embeddings in two different ways. The first is a text-based species encoder, (t), with parameters d. which takes text t from our Wikipedia text data as input. The second species representation is batch of species tokens optimized directly. E E Sx26 Given a known species y E {1..... ... 5) in the observation dataset, we can generate its representation with a simple lookup. Ey. Since we learn a unique species token for each species in the training set, these species tokens cannot be used in the zero-shot setting. However, we are able to maintain the ability to have true supervised evaluation on species seen at training time. Additionally, the species tokens are used when a species has no text description.
Our text-based species encoder has two parts: a frozen LLM used to extract text embeddings and a learned fully connected network. As mentioned previously, to perform zero-shot SDM, we cannot directly utilize the species tokens. Instead, we input segments of text sourced from Wikipedia articles. Since these text segments can be as long as multiple paragraphs, we utilize pretrained Large Language Model, GritLM 224, to create fixed-length text embedding. GritLM is a recent language model with strong performance on text embedding tasks like document classification and retrieval. Due to the length of text in our data, it is much easier to work with a fixed-length embedding than with per-token embeddings from generative models like Llama3 [. Given this text embedding, we then learn three-layer fully connected language encoder network that outputs the final species embedding. As our approach incorporates language within the SINR framework for range estimation, we call it: Language Enhanced SINR (LE-SINR).
4

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3.4 Training LE-SINR
During training we only have access to presence observations, ie.. locations where species have been observed, and do not have any absence observations, i.e., locations where species have been confirmed to be absent. As a result, we train our model with a modified version of Lun.FuLL from SINR [TO] which one of the best-performing losses in their experiments. It minimizes binary cross-entropy with positives sampled from the observation dataset and negatives (i.e., 'pseudo absences') selected be all other species at the same observation location, as well all species at uniformly sampled random location.
This loss is computationally expensive with model, it requires computing a species embedding for every species and sample in the batch. To reduce this computation, we perform an approximation by selecting .M- - random negative species the observation location and M random negatives from the random location, where M < S. We also weight the negatives by the inverse of the proportion selected. This ensures that the expectation over the randomly selected negative species equals the original loss. Our modified loss is given by:
M > 1=1
$ [ + |zy#1| M log(1 - 9z) +
CaN = -
log(1 - 85)] (I)
M
Here, ý E IRM are predictions at one location for the ground truth species and M - random other species, z E RM is the corresponding one-hot label, and ý' E R& are predictions for random species at random location uniformly sampled over earth. For all models, we set M = 192 based on memory and compute considerations. We also tried values as large as 2,048 but saw significantly slower training times with effeet zero-shot performance.
During training, we first use the location encoder to generate location features. We then use these to make two predictions: one using the species tokens and the other from the text-based species embeddings. Finally, we apply L'N--FuL. to both of these predictions independently. We not explicitly encourage the species tokens and text-based species embeddings to be close. In our preliminary experiments, this seemed to be too restrictive and hurt performance.
3.5 Evaluation
Similar to SINR, we evaluate our model using expert-derived range maps from the eBird Status & Trends (S&T) dataset [5). which covers 535 bird species with a focus on North America, as well as range maps for 2,418 species from the International Union for Conservation of Nature (IUCN) Red List [ During training, we exclude observations for these species to assess zero-shot and few-shot performance, measured using mean average precision (MAP), i.c., average precision (AP) averaged across all species in the set. SINR models trained with target species' observations provide an "upper bound" on performance.
Zero-shot evaluation. Our model naturally supports zero-shot evaluation by providing text prompt from species not in the training data to the species model. The output species embedding can then be multiplied with position features to generate the probability of occurrence. We can then compute a precision-recall curve by varying the threshold over the score to generate the range map given an expert derived range map. We again report the mean average precision (MAP) across species in the S&T and IUCN datasets.
An important choice here is which text we provide for the evaluation. The Wikipedia section names are not consistent across articles and often the same information can appear under different headings. At the same time, it is unlikely that one can provide such detailed text for novel species. To standardize the information for realistic evaluation, we use the open-source Llama-3 model [ to generate two short summaries: a habitat description and a range description from the article text. The range description is most informative as it typically lists specific countries or regions where the species can be found. practice, such a rich description might not be available, so we also generate a habitat description. During evaluation, we use these summaries instead of the Wikipedia text. Figure shows some example summary texts along with zero-shot range predictions from LE-SINR. Further examples can be found in the Appendix.
5

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Few-shot evaluation. While the original SINR model was trained with observations from the target species, we also consider setting where position features are used derive SRMs from sparse observational data. We achieve this by performing logistic regression with L/2 normalization on the position features to predict presence or absence. For each species, we sample up positives from the observation dataset and nh, 20, 000 negatives. To mimic the ANN-FULL loss, we sample 10,000 negatives uniformly across the Earth and 10,000 negatives randomly from the training dataset species locations. Our logistic regression loss is given by,
up npp Σ
1
1 Σ log [1 - olwo [[]]o([((n))) nyd ||w 12
Treg
log [o(w] [(xi))
(2)
nh
where w E R256 is the species parameter being optimized, À is the regularization strength, d = 256, is the sigmoid function, fx,) the output of the position branch for the i-th positive observation, and (n.) is the output of the position branch for the i-th negative observation. We use this loss to estimate SRM when text not provided test time. The learned weights can then be applied to all positions to derive range map.
To incorporate text test time along with observational data, modify the regularization be the distance from the predicted text-based species embedding, resulting in the following loss,
Treg.tx
log [a(ww] [(x:))
log [1 al-aww' [(n))) + nyd w wix (3)
7hp
Th
where Wn. is the output of the text-based species encoder when provided text summary. This encourages the learned species weight vector to be similar to the text derived one. This approach is simple to implement, and our experiments indicate that it is also effective.
3.6 Implementation Details
We closely follow the hyperparameters from SINR for a fair comparison. We train with the Adam optimizer for 10 epochs with learning rate 0.0005. The species network has three linear layers with ReLU activation. The input text embedding dimension is 4,096, the hidden dimension is 512, and the output species embedding dimension is 256. During training, at each iteration, we choose a random Wikipedia section to generate each species embedding. For logistic regression in the later few-shot evaluation experiments, we use a regularization strength À = 20. Training a single LE-SIN LE-SINR model from scratch using all the text and observational data takes about 10 hours single NVIDIA RTX 2080ti GPU occupying about 10GB VRAM. Wikipedia text embeddings and their summaries generated once using distributed GPU cluster.
Results
4.1 Zero-shot Range Estimation
In T we compare against several baseline methods to establish performance lower and upper bounds as zero-shot range prediction is a novel task. The constant prediction baseline assumes species is present everywhere, while model mean predicts the mean species distribution map for all species. We also show the 'oracle' performance obtained by training SINR which sees observations from evaluation species at training time. All models were trained with observations capped at 1, 000 with uniform negatives, using the AN full loss and '+Env' indicates models that are trained using extra environmental features input, in [10]. We compare these zero-shot range estimates obtained using various LE-SINR models and input text.
The results show that the zero-shot range estimates comfortably outperform the baselines, achieving non-trine non-trivial performance with no observations. Range text works the best, but even habitat text achieves strong performance on both IUCN and S&T species. Similar to SINR, we observe a boost when including environmental covariates. LE-SINR with explicit species tokens matches the Oracle SINR performance, i.e., when evaluation species are used during training, suggesting that LE-SINR does not lose performance on observed species. Therefore, LE-SINR can be used both to explicitly model observed species and for zero-shot prediction from text for novel species.

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Table 1: Zero-shot Range Estimation. Our LE-SINR approach enables zero-shot range estimation given a range or habitat description for a particular species. All models were trained with a maximum of 1,000 observations per species and uniformly sampled negatives, using the AN full loss. "+Env' indicates models trained with additional environmental features as input, as in SINR [TO]. *+Eaal Sp. indicates models that had observed data from the evaluation species during training. LE-SSNNR matches Oracle SINR performance when explicit species tokens are available, i... the model is trained with evaluation species, and we also show the performance on the observed species using the text-based species encoders for completeness in the Appendix.
Method
+Env
+Eval Sp.
IUCN
S&T
SINR
0.67
0.77
LE-SINR (Species Token) SINR
0.69
0.78
Oracle
0.76
0,80
LE-SINR (Species Token)
0.75
0.80
Constant Prediction
0.01
0.22
Baselines
Model Mcan
0.09
0.35
LE-SINR (Habitat Text)
0.28
0.51
LE-SIN SINR (Range Text)
0,47
0.61
Zero-shot
LE-SINR (Habitat Text)
0.32
0.52
LE-SINR (Range Text)
0.53
0.64
Table 2: LE-SINR Ablations. Impact of various design choices, such as species description (range and habitat vs. taxonomy in LD-SDM (28). location encoders, and the loss function on the zero- shot performance of LE-SINR. All models were trained without 'Eval Sp" and using 'Env' when
appropriate. LD-SDMT is our model and text encoder but trained with taxonomic text as in (8
Ablation Type
Method
+Env
IUCN
S&T
LD-SDM (2
(Genus Text)
0.19
0.30
Text
LD-SDM (Species Text)
0.21
0.33
GcoCLIP Backbone [33 (Habitat text)
0.23
0.49
GeoCLIP Backbone (Range Text)
0.41
0.58
Position Encoder
Spherical Harmonies Encoding [27] (Habitat Text)
0.31
0.52
Spherical Harmonics Encoding (Range Text)
0.53
0.63
SatCLIP Loss [18] (Habitat Text)
0.26
0.52
Loss Function
SatCLIP Loss (Range Text)
0.46
0.63
Ours (Habitat Text)
0.32
0.52
Ours (Range Text)
0.53
0.64
Figure compares our zero-shot performance with few-shot range estimation using logistic regression models trained with varying numbers of observations. On S&T the zero-shot performance with habitat text is worth two observations, while range text is worth ten. Figures 4 and A2 show range maps generated from text inputs for various species.
In Table 2 we compare against other training text, position encodings, and loss functions. For the LD-SDM (8 baseline we use LE-SINR architecture but replace the milkipedia-derived text with LD-SDM taxonomic classification strings. Unlike LD-SDM's autoregressive LLM, which generates an embedding for each token, this baseline, like our main method, encodes text using model trained specifically for fixed-size text embeddings. For each species, we construct a string representing different levels of the taxonomic hierarchy, including class, order, family, genus, and species. During training, we randomly select one of these strings in each iteration. We then perform zero-shot evaluations at each taxonomic level. Performance decreases as we move up the hierarchy with species-leve level text performing the best. For the position encoder baselines we first replace our position encoder with the pre-trained GeoCLIP training. We also try the Spherical Harmonics 27 coordinate encoding to encode the latitude and longitude. We experimented with multiple values for L well as the SIREN network architecture and found L 10 with our original MLP architecture worked best for this experiment. For the SatCLIP [8] baseline, we use our model but replace the AN loss with their contrastive loss applied to the position and text embeddings. Looking at Table 2 none of these experiments perform better than our method.
33 model. The weights are then fine-tuned during
4.2 Text-driven Priors for Few-shot Range Estimation
In addition to zero-shot range map generation, we can also use LE-SINR in the few-shot setting, i.e., when there are small number of location observations available for previously unseen test species.
7

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Text & Observations (IUCN)
Text Observations (S&T)
Observations only (IUCN and S&
MAP of Range Estimation
0.8
0.8
0.8
07
0.6
0.5
0.5
0.-
LE-2NR grange
LE-5NR (range tedd)
LE-SNR (UCCM)
LE SINR 0hab to Bex3
LE LESRR Juub Soxtl
5RV (UCN)
SHN
L5: SINA (55.7)
SIAR toitiig wt (OOK)
SPA Gaint tminkng wo 567)
SBV (567)
0.3
160 3000
10
100
1050
1.00
1.000
Observations per Species
Observations per Species
Observations per Species
Figure 3: Range Estimation from Text and Observations. (Left) IUCN and (Middle) S&T results for zero-shot range estimation based on text, and few-shot estimation based on the text-driven prior. Both range and habitat texts improve few-shot performance over baseline SINR. (Right) Comparison of the position branch of SINR and LE-SINR for range estimation using few examples. Language- driven covariates learned by LE-SINR lead to better generalization when observations are limited. We report the MAP for range estimation for species in the S&T and IUCN test sets.
Figure 3 shows few-shot results obtained using the text-driven prior from LE-SINR, as described in Section 3.3 on IUCN (left) and S&T (middle) species. Logistic regression models regularized toward species weights obtained from range and habitat text provide a significant boost over vanilla logistic models regularized toward zero. The gap is significant when the training data is limited, i.e., fewer than 100 observations. Range text priors reduce the need for observations on IUCN and S&T by a factor of 3x and 10x, respectively. Figure A2 in the Appendix shows some qualitative examples of how range estimates change with different numbers of observations.
4.3 Evaluation of Learned Positional Embeddings
Prior work on generating SRMs often rely on carefully-selected spatial covariates such as tempera- ture, elevation, precipitation, land cover, etc., to predict the distribution of species based on a few observations. Our approach provides a way learn rich set of spatial covariates based on language data. Figure 3 (right) compares the position embeddings of SINR and LE-SINR as spatial covariates on the task of few-shot range estimation. In both cases, we use a model whose location branch is trained with position only (i.e., only latitude and longitude) as input to avoid conflation of the learned covariates with input ones. We find that the intermediate position embeddings learned when trained using language lead to better generalization, especially when training data is limited. Note that here we do not use any language input at test time, as both models are trained simply using observation data, same as the logistic regression baselines.
Figure A in the Appendix shows that the position embeddings of LE-SINR have richer spatial structure than SINR. This figure was obtained by projecting the learned position embeddings to three dimensions using Independent Component Analysis and visualizing them as color in RGB space. Figure 5 visualizes a variety of maps generated from natural language. LE-SINR has learned about geographical regions, climate zones, and even abstract non-species concepts by aligning text representations with geographic locations through species observations. For example, the presence of a species such as the 'Fennec Fox' allows the model to learn that the species is associated with concepts such as the deserts of North Africa, particularly the Sahara Desert, sandy environments, and extreme temperatures based on information Wikipedia text.
4.4 Limitations and Broader Impacts
While we show that language and location observations can be combined to estimate range maps from few examples, we do not compare to other few-shot methods such as those based on meta- learning 229 20 [6]. However, recent results for non-species range estimation tasks indicate that a good underlying representation is a key component to superior few-shot performance, even outperforming more advanced methods [17.
Another limitation is that our models are most applicable when range and habitat descriptions are available with very few observations, which is not common. While we evaluate our approach based on Wikipedia, more realistic scenario involves obtaining descriptions of rarely observed species from domain experts. However, we note that many species have substantial Wikipedia articles despite having fewer than 50 observations on iNaturalist.
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The byacinth macaww prefers semi-open, somewhat wooded habitats. usually avoids dense, humid forest, and regions dominated by such habilats. it is generally restricted the edge or relatively open sections (e-g.. along mujer rivers). In different areus of their range, these parrots found savannah grasslands, dry forests known us caatingu, and in pulm stands or swamps, particularly the moriche palm (Mauritia flexuesa).
The hyacinth macaw occurs today in three main areas in South Amer- ica: Pantanal region Brazil, and adjacent castern Bolivia northeastern Paraguay, in the cerrado regions of eastern interior of Bruzil (Maranhão, Piaui, Bahia. Tocantins, Goiss, Mato Grosso Mato Grosso Sul, Minas Gerais), areas ussociated with the Tocantins River, Xingu River, Tapujós River, the Marajó in eastern
relatively open
Brazil.
They are diurnal, lerrestrial, and live complex, mixed genderr social groups of 8 to 200 individuals per troop. They prefer savannas and light forests with climate that suitable for their omnivorous diet. Habitar text description
Yellow babouns inhabit savannus and light forests in eustern Africa, from Kenya and Tanzania to Zimhabwe and Botswana.
Range text description
Figure 4: Zero-Shot Range Estimation. Here we show the 'Habitat' and 'Range' text descriptions and corresponding zero-shot range maps for the Hyacinth Macaw (top) and the Yellow Baboon (bottom), with expert derived range maps inset.
Our models rely on text embeddings and summaries generated from LLMs, and thus may inherit the biases contained within them. For example, our model could further amplify biases in the data by incorrectly spatially localizing specific text terms inappropriately. Both Wikipedia text and observational data from iNaturalist are biased toward the United States and Western Europe. As result, our models may not generalize as well to other geographical regions. There could also be potential negative consequences associated with using the species range predictions from model to inform conservation or policy decisions. While our results are promising, they may still fall short of the quality needed for such high-stakes cases. Therefore, caution is encouraged when making decisions based on the model's predictions. Another risk is that the model could be used to locate threatened endangered species. To mitigate this, only train publicly available observation data that has been deemed safe to redistribute by the iNaturalist platform.
5 Conclusion
The generation of detailed species range maps is often constrained by the need for extensive location observations, which can be expensive and time-consuming to collect. Our LE-SINR approach mitigates this issue by mapping species observations and text descriptions into the same space, enabling zero-shot range map generation from text alone. Unlike other methods that make use of text, we can generate global range map for species with single forward pass of our language model, significantly increasing usability for potential downstream users.
Our extensive evaluation shows that zero-shot range maps produced by LE-SINE derived solely from text descriptions, can outperform those produced by state-of-the-art models trained on tens of observations. Additionally, using LE-SINR performance. By learning to relate text descriptions of species with locations where that species has been observed, we show that LE-SINR develops an understanding of wide range of geographical and environmental features, as well as unrelated concepts not seen in the training data such as historical events and aspects of culture.
prior also significantly enhances few-shot

---

"europe"
"madagascar"
"Andes"
"great lakes"
Q "Treaty of Waitangi"
Q "hello kitty"
Q
"Babe Ruth" "
Q
"Simón Bolívar"
Q "africa"
Q "desert"
Figure 5: Geospatial Grounding of Non-Species Concepts. LE-SINR is able to geographically
ground text prompts to locations on the earth. Here we display the inner product between the location encoder's 's features and the language model's 's encoding of the text displayed in the bottom left of each panel. This includes coarse concepts such as continents and countries (top row), geographic features such specific lakes and mountain ranges (second row), in addition to concepts that do not appear our species text training data but are likely already represented in the language model (third and fourth row). We do, however, observe some limitations resulting from the biases in training data which favors North America, Europe, and Australasia (final row). Please zoom in to see more detail.
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Acknowledgments and Disclosure of Funding
We thank the iNaturalist community for providing the data used for training models. OMA was in part supported by a Royal Society Research Grant, SM and MH are supported by grant #2329927 from the National Science Foundation.
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Appendix
A Additional Results
In Table Al we evaluate our oracle model, that was trained on evaluation species observations, on the habitat and range text summaries. These results show that some of the performance gap between the zero-shot and supervised methods can be explained by inherent ambiguities of our evaluation text descriptions to describe the range.
In Figure AT we display the intermediate representations learned by our location encoder and compare it the standard SINR model that does not make use of any language information. There appears to be more higher frequency spatial information encoded in our representations which consistent with the improved few-shot performance observe when using these features in conjunction with logistic regression classifier in Figure 3 from the main paper.
In Figure A2
show the range estimates obtained from text (zero-shot setting) as well as a few examples (few-shot setting corresponding to Table ffrom the main paper) for three different species, namely the Northern Yellow- wwww-sshllldedee Bat, Lark Bunting, and European Serin along with the associated range maps curated by experts.
In Figure A3 we show the range estimates obtained from both habitat and range text embed- dings (zero-shot setting) alongside the associated range maps curated by experts for six additional species, namely the Striated Babbler, Striped Sticky Frog, Common Hawk-Cuckoo, Cape Griffon, Madagascar Hoopoe and Raucous Toad.
In Figure A4 we show how zero-shot range estimates for the Collared Bush Robin change as we use different parts of piece of text.
Table Al: Evaluation Text Oracles. We can get tighter upper bound performance by evaluating our oracle model with the habitat and range text summaries rather than species tokens. These summaries are not seen in the training data.
Method
+Eval Sp.
IUCN
S&T
LE-SINR (Habitat Text)
0.32
0.52
LE-SINR (Range Text)
0.53
0.64
LE-SINR (Habitat Text)
0.39
0.61
LE-SINR (Range Text)
0.60
0.69
B Dataset Examples
Below we show 'range', 'habitat' text for several species used for our zero-shot range estimation experiments. These were generated by using Llama3 language model to summarize the Wikipedia text into content that describes the range, i.c., the geographical extent where the species is found, including names of countries, continents, and geographic regions, well as its habitat, which indicates specific environmental conditions and types of habitats that a species thrives in such as descriptions of climate, vegetation, topography, soil types, food resources, etc.
1. Gray Kingbird (Tyrannus dominicensis)
The gray kingbird is found in the southeast USA, Colombia, and Venezuela, with recognized subspecies: T. d. dominicensis and T. d. vorax. breeds from the extreme southeast of the United States, mainly in Florida, as well as Central America, and through the West Indies south Venezuela, Trinidad and Tobago, the Guianas, and Colombia. Northern populations are migratory, wintering on the Caribbean coast of Central America and northern South America.
Range:
Habitar: The gray kingbird favors tall trees and shrubs, including the edges of savanna and marshes. It found in increasing numbers in the state of Florida, and is more often found inland though it had been previously restricted to the coast.
2. Cape Weaver (Ploceus capensis)
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SINR
LE-SINR
Figure Al: Visualization of the intermediate position representation learned by the network projected to three dimensions using Independent Component Analysis. (Top) Standard SINR model. (Bottom) Our LE-SINR model. Both models were trained with position features as the only input.
Range:
The Cape weaver is endemic to South Africa, Lesotho, and Eswatini, occurring across much of the area excluding the Kalahari Desert from the Orange River in the Northern Cape south to the Cape of Good Hope, then east to northern KwaZulu Natal, and inland almost to Bloemfontein the Free State.
Habitat: The Cape weaver occurs in open grassland, lowland fynbos, coastal thicket, and farmland, so long as there is permanent water and trees. In the more arid, hotter regions, it is restricted to upland areas and never occurs in forest.
3. Plate-billed Mountain-Toucar (Andigena laminirostris)
The plate-billed mountain toucan is found in the western foothills of the Andes of western Ecuador and far southwestern Colombia, specifically from Pita Canyon (Narino) in southwestern Colombia and south to the northwestern border of Morona-Santiann Province, Ecuador.
Range:
Habitar: The species inhabits the humid forest and edges of the temperate forest of the lateral slope the Andes Mountains, featuring abundant epiphytes, bromeliads, and mosses. The forests receive an average of 14 feet of rainfall per year, and the canopy ranges from 10 meters high. Their altitudinal range is between 1600 and 2600 meters above level.
4. Harlequin Racerunner (Plica umbra)
The blue-lipped tree lizard or harlequin racerunner (Plica umbra) is found in South America, specifically Colombia, Venezuela, Guyana, Suriname, French Guiana. Brazil, Bolivia, Peru, and Ecuador.
Range:
Habitar: The species inhabits tropical rainforests, savannas, and dry forests, typically at elevations below 500 meters. prefers areas with dense vegetation, rocky outcrops, and sandy or clay soils.
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zero pero-Bhot -hot
one shot
five shot
ten shot
Expert Range
Northern Yellor-shouldered Bat
Lark Bunting
European Serin
Visualization of Zero-Shot and Few-Shot Range Estimates. Range estimates obtained from text only (zero-shot) for three different species shown on top row. Range estimates with one, five, and ten observations are shown in rows two, three, and four respectively, while the ground truth range shown the bottom row along with the name of the species. Zero-shot predictions were made from the 'range' text. Zoom in for details.
Figure A2:
5. Dusky Rattlesnake (Crotalus triseriatus)
Range: The Mexican dusky rattlesnake (Crotalus triseriatus) is found in Mexico, along the southern edge of the Mexican Plateau in the highlands of the Transverse Volcanic Cordillera, including the states of Jalisco, México, Michoacán, Morelos, Nayarit, Puebla, Tlaxcala, and Veracruz.
Habitat: Crotalus triseriatus occurs in pine-oak forest, boreal forest, coniferous forest, and bunchgrass grasslands. On Volcán Orizaba, species being found within the zone where the snow line comes down about 4,572 (15,000 ft), and green plants can be found up to 4,573 m (15,003 ft).
found at very high altitudes, with the
6. Western Ghats Flying Lizard (Draco dussumieri)
Range: The species is found principally the Western Ghats and some other hill forests of Southern India, including Karnataka, Kerala, Tamil Nadu, Goa, and Maharashtra. is also reported from some parts of the Eastern Ghats in Andhra Pradesh.
Habitar: The southern flying lizard almost entirely arboreal, found on trees forests and adjoining palm plantations. climbs trees in search of insect prey the trunks and leaps off when reaches the top to land on adjoining trees. The species active during the day after it has warmed up in the early morning sun.
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Striated Babbler
Striped Sticky Frog
Common Hawk Cuckoo
Cape Griffon
Madagascar
Hoopoe
cous Toad
Habitat text
Range text
Expert Derived Range
Figure A3: Additional Zero-Shot Range Estimation. Here we show the 'Habitat' (left) and 'Range' (center) text zero-shot range maps with associated expert derived range maps (right), for variety of species. While the 'Range' text provides a strong prior in all cases with high probability assigned to regions within the expert derived range, the 'Habitat" text does not always do this, with the Striated Babbler, Striped Sticky Frog, and the Raucous Toad providing strong prior. Zoom in to see details.
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Habitat Text
Range Text
The species inhabits undergrowth of coniferous forests with shruhs and bamboo, and can also be seen in parks and along ronds.
The collared bush rohin endemic to Taiwan,
typically resides elevations of 2,000 2,800m (6,600 9,200 ft) and sometimes above the tree line, descending to lower elevations
living montame and subalpine forests.
winter.
The species inhabits undergrowth of coniferous forests with shrubs banboo. also seen parks along typically resides at elevations of 2,000 to 2,800m (6.600 to 9,200 n) sometimes above tree line, descending lower elevations in winter.
The collared bush robin is endemic to Taiwan, living in montane subalpine forests.
Figure A4: Zero-Shot Range Estimation From Parts of Text. Here we show the zero-shot predicted ranges when different parts of the habitat text (left column) and range text (right column) are given our model for the Collared Bush Robin, which found in Taiwan. From top to bottom, the rows show the predicted range for the first part of the text, the second part of the text, and the entire text, i.e., the first and second parts concatenated. For the habitat text we see that the first part of the text loosely identifies several forested areas but not Taiwan, while the second part loosely identifies several mountainous areas including Taiwan. Combining these parts reduces the false positives in the range map produced while still correctly including Taiwan. For the range text, again see that the second part of the text identifies some mountainous areas, while the first part seems to locate Taiwan effectively. Together the range is correctly limited to the inland highland areas of Taiwan. Please zoom in to see more detail.
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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Section predicted range maps using expert-derived range maps for species in the S&T and IUCN lists. We show the value of language supervision by demonstrating zero-shot range estimation from descriptions of habitat and range preferences. We also demonstrate the value of learning from language with superior performance over the baseline SINR model trained with observations only in the few-shot setting
4 provide detailed experimental evidence quantifying the accuracy of
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If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (c.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: While we do scrape text from the internet, the datasets do not contain images, and are less extensive focusing on a few thousand Wikipedia articles covering a wide range of species in natural world. Thus believe it poses low safety risks such as those associated with unsafe images or text.
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
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Question: Are the creators or original owners of assets (e.g.. code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: Our benchmark builds on data from iNaturalist and Wikipedia. The iNaturalist data already publicly available from the original SINR paper, as we noted in our paper. Meanwhile, Wikipedia grants permission to copy, distribute and/or modify Wikipedia's 's text under the terms of the Creative Commons Attribution-She We will include these details in the public release of our benchmark.
Alike 4.0 International License.
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this information is not available online, the authors are encouraged to reach out to the asset's ss creators.
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Answer: [Yes]
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The paper does not involve crowdsoureing nor research with human subjects.
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According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
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Answer: [NA]
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Reviews:
[{"review_id": "7JixcEPp4q", "reviewer": "Reviewer_2RN9", "summary": "This paper considers the problem of species range maps (SRMs) estimation and proposes to combine textual descriptions of habitat or range of species from Wikipedia with geolocated citizen science species observations, building on the SINR model. The method is evaluated in the context of zero-shot and few-shot estimation of SRMs for eBird S&T and IUCN species. \nThis is an interesting idea to tackle the problem of SRM estimation for species with few observations, some results seem promising but I have some concerns and questions about the experiments and choice of the evaluation tasks. I am open to revising my score if the author clarify them.", "questions": "In addition to concerns/questions in the Weaknesses section, I have the following questions: \n\n- In section 3.2, it is mentioned that “not all species in our observation  dataset have an associated text description”.  Can you clarify how the  species with no text descriptions are handled? I understood that only the position branch is used in that case, is that right?\n\n- Have the authors done some analysis of whether species with certain habitats/geographical ranges are better predicted?", "limitations": "The authors clearly acknowledge the limitations of their work.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is well-written.\n- The problem considered is important, and the angle of looking at few-shot and zero-shot learning for SRMs has so far been overlooked in the literature. \n- The paper proposes a novel multi-modal approach to species range mapping. \n- The related works section is comprehensive. \n- The figures are well presented and illustrate the main points of the paper well.", "weaknesses": "**Motivation and use case:**\n\nI am unsure of the motivation of estimating SRM based on “textual descriptions that might be known to an ecologist” of range observations. It seems like the map is already self contained in the description of range which seem quite detailed and an end user to this model would probably be able to draw a range map based on the textual description of the range, or not gain additional information from having a map produced by LE-SINR  compared to the textual description which is reliable already. \nI am happy to hear more about the use cases the authors had in mind when developing this method. \n\nOn the other hand, using habitat information is an interesting proposition but habitat suitability maps are not range maps, and there are inherent limitations to using habitat descriptions to estimate range, and can lead to some problematic predictions if species have the same habitat (but not the same range) and those are usually treated in ecology as different problems. That being said, I have noted that the examples presented in Figure 4 with habitat text description show that the model is able to “restrict” geographically the range of species successfully to the relevant continents, without any postprocessing I suppose (?). \n\n\n**Baselines**\n\nThe baselines seem a bit simplistic. I would have expected another baseline to be a model trained on environmental variables from WorldClim for example, in order to have some comparison to a model  that is not just the mean species distribution map for all species or constant prediction. Especially given that all models compared to LE-SINR have the eval species in the training set, it seems it would be possible to make more realistic baselines. \nBut maybe I missed a point and therefore:\nCould the authors describe how  the mean species distribution map obtained? \n Could the authors describe what the baseline model mean _env +eval Sp consists in ? \n\n**Results:**\n\nIt seems that the methods seems most advantageous in the zero-shot setting (over whose motivation I raised some concerns about in the “motivation and use case” point) and performance in the few shot setting is not very convincing. In 4.2, it is highlighted that one of the reasons might ne that “the logistic regression models are trained independently for each target species using uniform negative samples,  the original SINR model trains all the species together, benefiting from other species observations to capture the negative set.” It is appreciated that the authors highlight this difference, and it would make a stronger paper and comparison if the SINR design choices for capturing the negative set were kept for LE-SINR (and perhaps give more convincing results). \n\n**Minor comments:**\n\nNo error bars are reported but the authors provide a justification for that in the paper checklist\n\n\nThe paper has some good ideas, and some examples shown seem to point to the potential of the proposed method but the choice of the evaluation tasks is questionable given the information present in the paper, and it seems that the main highlighted advantage of the method is that fact that it can be applied in a zero-shot setting, and the method does not seem particularly advantageous in comparison to existing methods in other settings. \nI am open to revise my score, but would need more details on the motivation for this choice of evaluation tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[2RN9-1] Motivation and use case.**   \nThe primary motivation of our work is to leverage an additional data modality, text, to improve both zero-shot and few-shot species range mapping. We observed that text data, as formatted on Wikipedia, often includes descriptions, habitat information, and range information for species. We acknowledge that the presence of detailed range descriptions can simplify, and in some cases trivialize, the process of producing a range map for a species. This is precisely why we also conducted experiments using only habitat information. While performance decreases compared to using range descriptions, we demonstrate that incorporating habitat text still offers improvements over previous methods (see Fig. 3). \n\nAs a motivating use case, we envisioned a scenario where a scientist, recently returned from the field, describes the habitat in which they observed a possibly new species and uses our method to visualize a plausible range map. In practical applications, the maps generated by our method are intended to serve as a starting point for further refinement. This initial starting point can be valuable for generating detailed maps and guiding more in-depth studies. A large percentage of species on platforms such as iNaturalist only have a small number of observations, but do have Wikipedia pages, and thus would benefit from better few-shot range estimation. Furthermore, some species are presumed extinct, have no modern observations, but still have text descriptions on Wikipedia (e.g. “New Caledonian owlet-nightjar”). \n\nWe understand that the use of habitat text could conceivably create output maps that show the “fundamental ecological niche” of the species rather than the range or the similar “realized ecological niche” of the species. In practice, as noted in the review, our model does manage to “restrict” geographically. This is not due to post processing. Instead our model is able to infer the range from the habitat text due to mentions of other species, and specific features of that location. For example, for the hyacinth macaw, the habitat text ends with: \"these parrots are found... in dry thorn forests known as caatinga, and in palm stands or swamps, particularly the moriche palm (Mauritia flexuosa).\" World knowledge encoded in the LLM about \"caatinga\" and \"Mauritia flexuosa\" allows the model to successfully select from the many locations that fit the first line of the description of \"semi-open, somewhat wooded habitats\" and so correctly chooses South America as the likely home of this species.\n\n**[2RN9-2] Baselines.**  \nThe Model Mean model is the Oracle SINR model (trained with or without environment features), but whose output is the average of all species outputs (including or excluding the evaluation species) for each input. Model Mean +Env +Eval Sp. is therefore the Oracle SINR model trained with additional environmental features as input and is trained with observations from the evaluation species. We will clarify this in the text, sorry for the confusion! \n\nIn response to requests from other reviewers, we have included additional quantitative comparisons, e.g. LD-SDM (Sastry et al. arXiv 2023) and the contrastive SatCLIP (Klemmer et al. arXiv 2023) in response to *9uUS*, GeoCLIP (Cepeda et al. NeurIPS 2023) and Spherical Harmonics (Rußwurm et al. ICLR 2024) in response to *CYPb*. See the responses to the other reviewers for results.  \n\n**[2RN9-3] Results.**  \nOur method is advantageous in *both* the zero-shot and the low-shot setting, where we are much better than the existing SINR. The results in the low-shot setting are particularly noteworthy, as we observe a consistent performance improvement all the way up to, and including, the 10 training observations per species setting. This is important as thousands of species on platforms such as iNaturalist have a limited number of observations. \n\nWe agree with your desire to control for the negative sampling process in our few-shot results. We provide these additional results in Fig. B1 in the rebuttal PDF. We observe the relative ordering of the different methods stays the same, and we still observe a large boost in performance from our method compared to SINR. \n\n**[2RN9-4] Error bars.**   \nThe results in Fig. 3 are averaged over hundreds of species, and as noted on L617 the standard deviation is very small. We can include them in the final revision if deemed important. \n\n**[2RN9-5] Species in evaluation set with no text descriptions.**   \nFor training species with no text descriptions we only use the learned species tokens, i.e. SINR approach. For evaluation species with no text description we just skip them and set the performance as 0. There are four evaluation species with no Wikipedia text.\n\n**[2RN9-6] Analysis of whether certain regions are better predicted.**  \nInteresting suggestion! Please see Fig. B2 in the rebuttal PDF for an investigation into how our performance is biased geographically. We observe that, perhaps unsurprisingly, our approach underperforms in regions with limited training data (e.g. central Africa), with a particularly high error for Lake Victoria. As there are very few training examples in this region our model has almost no understanding that this lake exists and gives similar predictions for the lake and surrounding land areas, despite the large difference in species present on land and in water.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "L72FYGyrfy", "reviewer": "Reviewer_A4uw", "summary": "The authors extend the SINR model for species distribution modeling (SDM) by aligning the learned, spatial, latent space with the representation of the species habitat/range provided by an LLM.\nThis addition allows the authors to evaluate their approach in a zero-shot setting, something that SINR or other SDM models are unable to do.\nTheir results also show the advantage of this method in a few-shot setting.", "questions": "1) I’m not sure why the loss that seems to be the one used for training, Eq. (2), appears in the section on few-shot evaluation, rather than in the loss functions section. I suggest that authors revise the order in which they present their method for understandability.\n\n2) I think page 9 could be better used than it is now. Although the mapped concepts are fun to see, I think they belong in the appendix. I would suggest to instead do a more relevant evaluation, for instance by using Köppen climate zone descriptions.", "limitations": "Limitations of the approach are addressed.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The approach is very simple and intuitive, and the zero- and few-shot results show that this has a good potential for usability with species that have few observations.", "weaknesses": "I would certainly like to see this work published, as I find their main contribution (evaluating the usefulness of species habitat/range text for SDM) interesting for researchers working on this topic. However, I do wonder whether this is the type of contribution the NeurIPS readership is looking for.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[A4uw-1] Within scope for NeurIPS?**   \nWe believe that NeurIPS is an appropriate venue for this work given similar work published at top ranked machine learning venues in the past. For example, NeurIPS (“Active Learning-Based Species Range Estimation” by Lange et al. and “SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data” by Teng et. al.), ICML (“A Maximum Entropy Approach to Species Distribution Modeling” by Phillips et al. and “Spatial Implicit Neural Representations for Global-Scale Species Mapping” by Cole et. al.), ICLR (“Geographic location encoding with spherical harmonics and sinusoidal representation networks” by Rußwurm et. al.), and AAAI (“Bias reduction via end-to-end shift learning: Application to citizen science” by Chen and Gomes). We believe that new methods for estimating the spatial range of species is a question of societal importance and requires input from machine learning researchers. Additionally, insights into how spatial information is encoded into LLMs is highly relevant to the machine learning audience. \n\n**[A4uw-2] Moving equation 2 for enhanced readability.**  \nThank you for the suggestion! We will make this fix. \n\n**[A4uw-3] Köppen climate zone descriptions.**   \nEvaluating more species related text descriptions such as Köppen climate zones is an interesting suggestion. As requested, we have included two zones in Fig. B3 in the rebuttal PDF. We observe LE-SINR is able to give plausible estimates of these zones, particularly where iNaturalist has reasonable training data coverage. However we observe false negative predictions in areas of low data coverage (e.g. central Africa in the figure on the right).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xJUSMfVyHU", "reviewer": "Reviewer_q7Tg", "summary": "The paper considers the problem of species range mapping, where the aim is to estimate, at any given location on the earth, if a particular species is present or not. The work builds on another very recent paper which developed \"Spatial Implicit Neural Representations\", where the aim is to estimate presence/absence of different species by encoding geographical information through neural network representation. This work extends SINR by utilizing textual information about the target species, which may be present in Wikipedia pages. For this purpose, they consider embedding of textual features using a LLM. The textual and geographical embeddings are fused to predict the presence/absence of species at different places, and this approach allows them to extend this to even those species for which there are very few or even zero observations.", "questions": "1) We would like to understand how exactly the geospatial encoding works in response to text prompts. In Figs 4 and 5 we see the spatial outputs in response to textual inputs - can we have some understanding of which parts of the text were most informative for the LLM in identifying the spatial locations?\n2) In most of the examples, we see that the locations highlighted on the map are very specific - typically one or two clusters in one continent. But how does the model work for mapping of species (or non-species concepts) which are common to many places across the world?\n3) In case of the zero-shot case, are the range maps limited to those locations that are explicitly mentioned in the text prompt, or can the LLM make additional inference also (since it has been pre-trained on a much wider corpus of text)?\n4) Can we use the concepts of ecology (related to predator-prey or symbiotic relations) as side information, to use the known information about presence/absence of some species at a location to infer the presence/absence of other species there?\n5) Similarly, if a species is known to be present in some types of habitats (based on climate zones, biomes etc), can we infer about their possible presence in similar habitats?\n6) Can we move from presence/absence detection to estimating population sizes of different species in different locations?", "limitations": "Since this work is related to conservation sciences, accuracy (especially precision/recall) and robustness are important considerations. It is not very clear to me if this work can be used to ascertain the conservation statuses of different species, but if it can be, the work may require a more sensitive treatment. Also, we need to understand if this work can be used by malicious players (eg. illegal poachers, smugglers) to perform operations that may endanger the biodiversity of the earth.", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper discusses species range mapping - a very important problem in conservation biology, ecology and other related disciplines. This is one of the early applications of ML in this domain, potentially opening up an entirely new area.\n1) The paper introduces LE-SINR, where textual data (wikipedia entries) are combined with observations to identify the presence/absence of species at different locations of the world\n2) The main technical contribution lies in the use of LLMs to extract geospatial information from text, and to encode such information into spatial locations on the map\n3) The work shows extensive experiments to validate their claims and develop useful maps. Apart from the main aim of species range mapping, these maps are also used to demonstrate the effectiveness of geospatial information extraction by LLMs and its embedding.", "weaknesses": "No weaknesses as such since this is a very new domain of application, but the paper throws up some unanswered questions, mentioned below:", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[q7Tg-1] Which parts of the text were most informative for the LLM?**  \nThis is a great suggestion. Given the space limitations we cannot provide a visualization of this in the rebuttal, but we will include this in the final revised paper. At a high level, text which more directly encodes information about the range is more informative than text that just describes the habitat (see results in Fig. 3).  \n\n**[q7Tg-2] Does the model work for species that are common to more than one location?**   \nJust like SINR, there is nothing conceptually stopping the model from predicting that a species occurs in more than one location on earth. In Fig. B3 in the rebuttal PDF we illustrate two examples of text that describe different climate zones that overlap with multiple different countries. We will include some additional species’ examples in the final revised text.   \n\n**[q7Tg-3] For zero-shot evaluation, can the model generalize beyond locations explicitly mentioned in the input text?**   \nYes. The example in Fig. B3 in the rebuttal PDF shows examples where specific locations are not mentioned, yet the model produces sensible predictions. Some of the examples in Fig. 5 in the main paper also demonstrate that the model is able to spatially encode non-species concepts from a small number of non-geographic words (e.g. “Hello Kitty” or “Babe Ruth”). We believe that exploring how LLMs spatially represent concepts is an exciting future research direction. \n\n**[q7Tg-4] Can we use additional ecological concepts as side-information?**   \nThis is a really interesting suggestion! Beyond the scope of this work, but it could be possible to use text related to species interactions (or lack thereof) to further improve the spatial representations we learn. Such knowledge is not likely explicitly codified at a large scale in a structured way online, but this makes natural text an ideal candidate to encode it.  \n\n**[q7Tg-5] Can the model infer species’ presence in similar habitats?**  \nYes. We believe that the model is already doing reasoning of this form. However, there are some possible limitations of non-spatially explicit encodings (i.e. only using environmental features and not spatial coordinates as input). When only using information derived from environmental habitats, it could be possible for a model to incorrectly predict that a species is present in that habitat across different continents (see “desert” example in bottom row of Fig. 5). \n\n**[q7Tg-6] Can we estimate population size instead of just presence and absence?**  \nThis is a longstanding and open question in statistical ecology, and out of scope for this initial work. This question has been studied extensively in the literature (e.g. Pearce and Boyce, Journal of Applied Ecology 2005), but without information about true absences or individuals it remains under constrained. However, proxies such as relative abundance can sometimes be estimated. \n\n**[q7Tg-7] Estimating the conservation statuses of different species?**  \nAs noted in Section 4.4, great caution should be exercised when using the outputs of our work for any downstream conservation assessments. While species range is an important factor in determining the threatened status of a species for repositories such as the IUCN Red List, other information is also needed (e.g. the list of threats they are susceptible to).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "P8MWppQKP0", "reviewer": "Reviewer_9uUS", "summary": "This paper presents LE-SINR which mappes species observations and textual descriptions into the same space and enables zero-shot inference for species range mapping for unseen species. The textual description of species are encoded with an LLM and used as a species embedding that jointly trained with location embedding for species range mapping.", "questions": "1. In few-shot evaluation, when you do the logistic regression, what part of model architecture is frozen?\n2. Figure 3, Is the left figure about zero-short evaluation? Then what does the x-axis of observations per species mean?", "limitations": "The author discusses about limitations and potential negative societal impact of their work in Section 4.4.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. Using free-form textual descriptions of species plus LLM to generate species embedding and trained with location embedding sounds like a logic next-step based on the existing work. The zero-shot ability is very attractive.\n2. The experimental setup looks very sounds.\n3. The geospatial visualization also hints on the meaning of learned location embeddings.", "weaknesses": "1. In Section 2, the author mentioned that the most relevant work of LE-SINR is LD-SDM. However, I do not see LD-SDM as one of baselines in the zero-shot and few-shot experiments.\n2. More ablation studies on the loss function on multimodal data are needed. The contrastive losses such as SatCLIP [1] and CSP [2] can be candidates of loss functions to be compared with the one used in the paper.\n3. Equation 1 and 2 look wired. The first term of both loss functions should only have one \"-\", right?\n\nMinor issues:\n1. Line 118: \"species x_i\" -> \"species y_i\"\n\n\n\n[1] Klemmer, Konstantin, Esther Rolf, Caleb Robinson, Lester Mackey, and Marc Rußwurm. \"Satclip: Global, general-purpose location embeddings with satellite imagery.\" arXiv preprint arXiv:2311.17179 (2023).\n[2] Mai, Gengchen, Ni Lao, Yutong He, Jiaming Song, and Stefano Ermon. \"Csp: Self-supervised contrastive spatial pre-training for geospatial-visual representations.\" In International Conference on Machine Learning, pp. 23498-23515. PMLR, 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[9uUS-1] Comparison to LD-SDM.**    \nWhile related, LD-SDM (Sastry et al. arXiv 2023) uses text data in a fundamentally different way to us. Instead of using unstructured text as input, their model generates an input string for each species that encodes its full taxonomic hierarchy (e.g. class -> order -> family -> genus -> species). Then for their *zero-shot* evaluation, they simply remove the final species name from the input text and evaluate on previously unseen species. Their core assumption is that they have seen species from that genus at training time. Our approach does not make such a strong assumption. \n\nAs requested, we performed an additional comparison to LD-SDM. We use the same architecture as our LE-SINR method with environmental feature inputs and simply change the training text. As our text encoder does not use causal attention, during training we randomly blank out different tokens to simulate the full taxonomic hierarchy not being present in the text string at evaluation time. In the table below, where the evaluation species are part of the training set, we crop the input text to different taxonomic levels during evaluation to simulate having coarser information (e.g. the “genus” row, corresponds to only including the full taxonomic name up until genus at test time). \n\n| LD-SDM  | IUCN   | SNT    |\n|---------|--------|--------|\n| species | 0.214 | 0.334 |\n| genus   | 0.192 | 0.298 |\n| family  | 0.127 | 0.246 |\n| order   | 0.067 | 0.207 |\n| class   | 0.047 | 0.19 |\n\nIn comparison, our approach performs significantly better.  \n\n| LE-SINR  | IUCN   | SNT    |\n|---------|--------|--------|\n| habitat  | 0.3198 | 0.5246 |\n| range   | 0.5332 |\t0.6363 |\n\n\n**[9uUS-2] Comparisons to contrastive losses.**    \nAs requested, we perform additional comparisons using a contrastive training loss, specifically the one from SatCLIP (Klemmer et al. arXiv 2023). During training we compute a contrastive loss between species and locations within a batch. We also extend this method (SatCLIP + negs.) by adding 50% uniform random locations as additional negatives. However, in both cases we see that it underperforms compared to our method. Additionally, our LE-SINR approach is compatible with different input encodings. See response to *CYPb* for further comparisons. \n\n|                       | IUCN - habitat | SNT - habitat | IUCN - range | SNT - range |\n|-----------------------|----------------|---------------|--------------|-------------|\n| SatCLIP               |         0.255 |        0.521 |       0.463 |      0.630 |\n| SatCLIP + negs. |         0.306 |        0.503 |       0.523 |      0.617 |\n\n**[9uUS-3] Equations 1 and 2.**    \nYes there is a minor typo here, thanks for flagging this. We will remove the extra minus at the start. \n\n**[9uUS-4] Which parts of the model are frozen for few-shot evaluation.**    \nDuring few-shot evaluation, the entire model is frozen (i.e. the location encoder $f(\\mathbf{x})$ and the text-based species encoder $g(\\mathbf{t})$). We only optimize the final classification weight vector for the species of interest. We will update Section 3.6 to clarify this.  \n\n**[9uUS-5] X-axis in Fig. 3 (left).**    \nAs indicated in the caption, this figure displays both zero-shot (i.e. “Observations per Species” = 0) and few-shot (i.e. “Observations per Species” > 0) performance. Only our language enhanced models are capable of producing zero-shot predictions. We will update the caption to make this clearer.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RaDqIk3kBY", "reviewer": "Reviewer_CYPb", "summary": "The paper introduces LE-SINR, a novel approach for estimating species range maps (SRMs) by combining citizen science observations with textual descriptions of species from Wikipedia. The proposed framework, an extension of previous work SINR, uses two branches one for location and one for text, for predicting the likelihood of a species being present at a given location and for a given textual description respectively. The location branch additionally incorporates learnable species embeddings to enable training in a fully-supervised setting. The work demonstrates LE-SINR outperforms baseline models in zero-shot range estimation, indicating its ability to generate plausible range maps from textual descriptions. Furthermore, LE-SINR shows strong performance in few-shot range estimation, highlighting its capacity to leverage textual information for enhanced accuracy when observational data is limited. The study underscores the potential of incorporating textual data for improving the accuracy and efficiency of species range estimation, with implications for ecology research, conservation, and planning.", "questions": "- Section 1 (Introduction) and Section 3.1 (Problem Setup), use the notation ‘location’ for representing (lat, lon). However, the rest of the paper uses ‘position’ for the same.\n- Section 3.3 is confusing to read. Writing should be improved.\n    * **L139-L143**, the text refers to species branch. What exactly is that referring to?  From Figure 2, it looks like the location encoder has two branches of computation. However, from the text it seems that the text encoder has two branches of computation.\n    * **L148-L152**, please clearly explain here that the final loss is formulated as a matching loss which maps as R^256->R^1. The model needs to perform S forward passes to compute the loss for each species category. That is why computing the original An-full loss is computationally expensive in this framework and a modified version is used as described in section 3.4.\n- I understand the formulation of loss in Section 3.4, however it is confusing to read. Please include an equation to highlight how the proposed loss is different from the loss used in SINR.", "limitations": "The limitations are adequately addressed in the paper.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The proposed framework is one of the earliest work to incorporate textual descriptions for species distribution modeling allowing zero-shot species mapping. The framework combines two training objectives: weakly-supervised learning (through text) and fully-supervised learning (through learnable species embeddings).\n- The authors compiled a novel dataset using Wikipedia articles containing a total of 37,889 species articles. In the future, the dataset can not only be used for other ecological tasks but also as a training/benchmarking dataset for large language models in the ecology domain.\n- The model has good few-shot and zero-shot performance as compared to previous baseline, SINR. \n- The flow of the paper in general is good and easy to understand.", "weaknesses": "- My main concern is that authors present zero-results of LE-SINR using range-text (includes region names) or habitat-text as input. The entire idea of species distribution modeling is to predict range of a given species. By providing range/habitat as text, the model can just \"cheat\". Further, as an end-user application, this kind of text may not be provided as input by a user.\n- In my opinion, some key results are missing from the paper:\n    * **L145-L147**: \"training both species representations jointly, we are able to achieve improved zero-shot performance\". By how much? Is it significant?\n    * Fully-supervised/orcale results for LE-SINR is not reported. It would be nice to compare SINR and LE-SINR using the same experimental setting, where species categories in the test set are also used during training.\n- Limited Technical Novelty: The proposed framework builds upon the previous work, SINR, which uses a ResNet MLP for location encoding. Such networks have been shown to lack the ability to capture high frequency spatial information. Recently, several location encoding frameworks have been proposed such as GeoCLIP [1], SH [2], etc. The paper does not compare the performance of different location encoding backbones.\n- Discussion and evaluation for what the text encoder branch has learned is missing in the paper. Does it learn the hierarchy present in the species taxonomy? Does the textual embeddings have some spatial correlation or patterns? Do similar species have similar textual embeddings?\n\n[1] Cepeda, Vicente Vivanco, Gaurav Kumar Nayak, and Mubarak Shah. \"GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization.\" In Thirty-seventh Conference on Neural Information Processing Systems. 2023.\n\n[2] Rußwurm, Marc, Konstantin Klemmer, Esther Rolf, Robin Zbinden, and Devis Tuia. \"Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks.\" In The Twelfth International Conference on Learning Representations. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[CYPb-1] The model is given range/habitat information as input.**    \nIt is true that the model is given this information as free-form input text. However, these range descriptions do not provide enough detail to draw a precise range map. For instance, knowing that the Gray Kingbird breeds in the \"extreme southeast of the United States, mainly in Florida\" (Appendix B, Example #1) is not enough information to draw a map of its range within the United States. What parts of Florida? And where in the southeastern U.S. outside Florida? \n\nWe demonstrate that it is possible for a model to make use of this relatively coarse text information to produce strong zero-shot and few-shot performance (see Fig. 3). This type of range information could indeed be readily available for a species that was historically recorded to science with no spatial observations or simply for species with very few observations (i.e. the vast majority of species on platforms such as iNaturalist only have a few observations and thus would benefit from better few-shot range estimation models). \n \n**[CYPb-2] Results when training with text only L145-L147.**   \nWe present the results for our model *without* the learned species tokens (“LE-SINR no species tokens”) for the zero-shot setting in the table below. Its performance is similar to our full model where these tokens are learned (“LE-SINR”). The advantage of including the learned tokens is that we can evaluate on those species directly if they are included in the training set. We will update the text on L145 to more precisely quantify their performance similarities in the zero-shot setting. \n\n|                         | IUCN - habitat | SNT - habitat | IUCN - range | SNT - range |\n|-------------------------|----------------|---------------|--------------|-------------|\n| LE-SINR no species tokens |          0.319 |         0.525 |        0.534 |       0.629 |\n| LE-SINR      |          0.320 |         0.525 |        0.533 |       0.636 |\n\n\n**[CYPb-3] Adding evaluation species to the training set.**   \nAs requested, we performed an additional experiment where we added the evaluation species to the training set (“LE-SINR w. eval species”) and compared these results to our model from the paper where they are excluded (“LE-SINR wo. eval species”). Perhaps unsurprisingly, adding the evaluation species improves performance. We will add these results to Table 1 in the revised text. Note, our performance is lower than the SINR baseline in Table 1 as we are only using LLM summarized text as input at evaluation time (L206) which differs from the training text (L186), whereas SINR gets to use the learned species tokens. \n\n\n|                          | IUCN - habitat | SNT - habitat | IUCN - range | SNT - range |\n|--------------------------|----------------|---------------|--------------|-------------|\n| LE-SINR wo. eval species |          0.320 |         0.525 |        0.533 |       0.636 |\n| LE-SINR w. eval species  |          0.384 |         0.610 |        0.598 |       0.685 |\n\n**[CYPb-4] Comparison to different location encoders.**   \nOur approach is agnostic to the choice of location encoder used. As requested, we performed additional experiments where we compare to GeoCLIP (Cepeda et al. NeurIPS 2023) and Spherical Harmonics (Rußwurm et al. ICLR 2024). In both cases, these encoders also make use of our LLM encoder. In the table below we observe that our LE-SINR approach outperforms these approaches. These results are perhaps not too surprising, because as noted in Table 1 (c) in Rußwurm et al. ICLR 2024, their spherical harmonic encoding does not actually perform better at the geo prior task (the one most closely related to range estimation task) compared to standard “wrapped” encoding that we use. Additionally, for the GeoCLIP experiment, where we start with their pre-trained network instead of our location encoder and fine-tune it, we also outperform them. This is also not surprising as their encoder is trained on web-sourced images that depict common everyday categories and are not necessarily specific to the natural world. \n\n|                     | IUCN - habitat | SNT - habitat | IUCN - range | SNT - range |\n|---------------------|----------------|---------------|--------------|-------------|\n| GeoCLIP             |          0.229 |         0.489 |        0.414 |       0.579 |\n| Spherical Harmonics |          0.309 |         0.518 |        0.528 |       0.626 |\n| LE-SINR             |          0.320 |         0.525 |        0.533 |       0.636 |\n\n\n**[CYPb-5] Discussion of what the text encoder has learned.**   \nThis is a good suggestion, we will include additional discussion regarding what our language encoder has learned. Regarding hierarchy, our comparisons to LD-SDM (see response to *9uUS*) indicate that simply encoding taxonomic hierarchy results in sub-optimal performance. In response to *q7Tg*’s question we also plan to illustrate which parts of the text are important to the model. \n\n**[CYPb-6] Use of the words “location” and “position”.**   \nThanks for flagging this, we will make our usage of the terminology more consistent. \n\n**[CYPb-7] Species branch (L139-L143).**   \nThe species branch refers to the two mechanisms we have for encoding species information: our text-based $g()$ encoder and learned species tokens $E$. We will refine this text and Fig 2 to make this clearer. \n\n**[CYPb-8] Clarification of loss function computation (L148-L152).**    \nWe will add the suggested text to improve the description in this section. \n\n**[CYPb-9] Description of training loss in Section 3.4.**\t\nWe will update the text in this section to make it easier to read and add an equation such that the comparison to SINR is clearer. Thanks for the suggestion!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Our work introduces LE-SINR, a new approach for geospatial grounding of free-form text. We apply LE-SINR to species range mapping, one of the most important problems in ecology and conservation policy. By integrating geospatial encoders with LLMs, LE-SINR achieves state-of-the-art performance on both zero-shot *and* few-shot species range mapping. Importantly, these findings demonstrate for the first time the potential of free-form, uncurated, text to improve species range mapping. Our models, code, and data will be released to support future work on this topic. 

There were two common themes in the reviews, which we address here. Other questions are addressed in individual responses to reviewers.   

## Evaluation  
First, reviewers requested additional comparisons and evaluation. We have included numerous new results and ablations in the table below, and have provided descriptions of each comparison in the responses to the individual reviewers. Our LE-SINR approach still obtains state of the art performance in all cases.  


|| IUCN(Habitat) | SNT(Habitat) | IUCN(Range) | SNT(Range) |
|:----|:---:|:---:|:---:|:---:|
|**Ours**|
|LE-SINR pos|0.285|0.510|0.469|0.607|
|LE-SINR pos+env|0.320|0.525|0.533|0.636|
|**Ours - Oracle (Eval Data in Train Set)**|
|LE-SINR pos Oracle|0.363|0.593|0.543|0.667|
|LE-SINR pos+env Oracle|0.385|0.610|0.598|0.685|
|**Species Representations**|
|LE-SINR no species tokens E|0.319|0.525|0.534|0.629|
|**Different Backbones**|
| GeoClip | 0.229 | 0.489 | 0.4143 | 0.5785 |
|Spherical Harmonics| 0.309 | 0.518 | 0.528 | 0.626 |
|**Contrastive Loss**|
|SatClip|0.255|0.521|0.463|0.630|
|SatClip + random negatives|0.306|0.503|0.523|0.617|
|**LD-SDM**|**IUCN**|**SNT**|||
|Species|0.214|0.333|||
|Genus|0.192|0.298|||
|Family|0.127|0.246|||
|Order|0.067|0.207|||
|Class|0.047|0.191|||

We first consider including the evaluation species and associated observations in the training data (i.e. Oracle). As expected, this improves performance in all settings. We next train without the joint species representations. In this case, we only use the text-based encoder without the species’ tokens. Performance is roughly the same as the joint representation method. That said, the species tokens in the joint representation allows for direct evaluation of species in the training set.

Next we compare against alternative backbones for the location encoder. For GeoClip, we initialize the location encoder with pretrained weights and finetune it. For spherical harmonics, we replace our standard “wrapped” encoding with the spherical harmonic encoding. Neither backbone leads to an improvement over our method. However, we note that our contributions are orthogonal to choices of encoder type and model architecture. 

We also evaluated the SatCLIP contrastive loss instead of our standard loss. For SatCLIP, we contrast locations and species within a batch. Since there are many locations that have no observations in the dataset, we also try a modification where additional uniformly sampled locations are included as negatives. This mimics the negative sampling strategy of our loss. In both cases we still see reduced performance compared to our method.

Finally, we train using the taxonomic hierarchy strings of LD-SDM instead of Wikipedia text. We report zero-shot results by withholding eval species from the training data. We can see that taxonomic information alone is not enough to perform well in the zero-shot setting. 


## Motivation
Second, reviewers wanted additional context for how our work could be used. Species range mapping is a long-tailed problem, which means that most species have very few observations (i.e. <50). Therefore, strong few-shot algorithms are critical if we want to understand the ranges of rare or difficult to study species. The main outcome of our work is a new method that achieves state-of-the-art performance on zero-shot *and* few-shot species range mapping by enabling users to provide text as input to the models. This text is free form, and can therefore flexibly incorporate the user’s knowledge, whether it is attributes of the species, habitat preference, or general range descriptions. Our method can utilize exclusively this text to generate a predicted range map (zero-shot), and we can take advantage of actual observations to refine the maps (few-shot). 

Reviewers asked if the zero shot situation is realistic? Consider presumed extinct species. We may well have range and habitat text from historic descriptions housed in museum collections, but we do not have access to structured observation data used in standard range estimation approaches. Our method can utilize the text to generate a candidate range map. Occasionally scientists also rediscover species that were previously thought extinct. In these cases we may have text data on range and habitat preferences alongside a small amount of observation data from the newly rediscovered species. Our proposed method fills an important gap in this problem space and gives scientists a new tool in their range estimation toolbox. Another practical use of our model is that it allows users to experiment with arbitrary text input to generate plausible occurrence maps. This capability is not restricted to just species range mapping but can also serve as a powerful tool for exploring where different habitats or environmental conditions might exist globally.  Additionally, this fusion of language models and location encoders allows us to probe and ground the spatial representations learned by LLMs.

Meta-review: This paper considers the problem of predicting species ranges from observational data drawn from iNaturalist, with auxiliary information provided in textual descriptions from Wikipedia. The reviewers agree that this is an important problem and that the solution proposed is novel, interesting, and useful. Accordingly, I think the paper is a clear accept.

I will note that while the application considered is indeed important, the authors somewhat exaggerate how often the few-shot paradigm comes up. In a rebuttal the authors state that "A large percentage of species on platforms such as iNaturalist only have a small number of observations, but do have Wikipedia pages, and thus would benefit from better few-shot range estimation." This is arguably false, since most species on iNaturalist with a small number of observations are likely e.g. invertebrates with no Wikipedia pages or merely stubs with no appreciable information. There is definitely a slice of species which are known well enough to have useful textual information but not enough to have good range maps, but it shouldn't be implied that this is a majority of under-studied species and it would be worth mentioning this explicitly in the paper when discussing limitations.
Decision: Accept (poster)